aszerW
6a5ae8efbf
Previously hardcoded equal weight (1/select_num), ignoring config weight type. Now reads position_weights from last daily_record, correctly showing rank-based weights.
163 lines
6.3 KiB
Python
163 lines
6.3 KiB
Python
"""IC Calibration diagnostic analysis script"""
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import os, sys, numpy as np, pandas as pd
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from pathlib import Path
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from collections import Counter
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os.environ['FLASK_API_URL'] = 'https://k3s.tokenpluse.xyz'
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sys.path.insert(0, str(Path.cwd()))
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from rotation.simple_rotation import SimpleRotationStrategy
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from rotation.ic_calibration import ICCalibrator
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def run_and_analyze(select_num, ic_enabled):
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s = SimpleRotationStrategy()
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s.config.rotation.select_num = select_num
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s.select_num = select_num
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if ic_enabled:
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s.config.rotation.ic_calibration.enabled = True
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s.ic_calibrator = ICCalibrator(fwd_days=5, min_samples=120, ic_method='pearson')
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r = s.run()
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return s, s.daily_records
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print("=" * 90)
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print(" Running 4 experiments...")
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print("=" * 90)
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s1, r1 = run_and_analyze(3, False)
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s2, r2 = run_and_analyze(3, True)
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s3, r3 = run_and_analyze(1, False)
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s4, r4 = run_and_analyze(1, True)
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# === 1. 持仓频率分析 ===
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print("\n\n" + "=" * 90)
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print(" 1. 持仓频率对比")
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print("=" * 90)
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for label, recs, s_obj in [
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("原始 select=3", r1, s1),
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("IC校准 select=3", r2, s2),
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("原始 select=1", r3, s3),
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("IC校准 select=1", r4, s4),
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]:
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hold_counter = Counter()
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for rec in recs:
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for h in rec['holdings']:
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nm = s_obj.config.asset_pools.assets.get(h)
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name = nm.name if nm else h
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hold_counter[name] += 1
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print(f"\n [{label}] 持仓天数分布:")
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for name, cnt in hold_counter.most_common():
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pct = cnt / len(recs) * 100
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print(f" {name:<12}: {cnt:>5} 天 ({pct:>5.1f}%)")
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# === 2. 关键资产选中变化 ===
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print("\n\n" + "=" * 90)
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print(" 2. IC校准 select=3: 关键资产被选中变化")
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print("=" * 90)
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targets = ['日经225', '德国DAX', '纳指100', '恒生指数', '黄金', '有色金属', '中证红利低波', '原油', '创业板指']
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for name_key in targets:
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orig = sum(1 for r in r1 if any(
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name_key == (s1.config.asset_pools.assets.get(h).name if s1.config.asset_pools.assets.get(h) else h)
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for h in r['holdings']))
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calib = sum(1 for r in r2 if any(
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name_key == (s2.config.asset_pools.assets.get(h).name if s2.config.asset_pools.assets.get(h) else h)
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for h in r['holdings']))
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print(f" {name_key:<12}: 原始={orig:>5}天 -> IC校准={calib:>5}天 (差={calib-orig:+d})")
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# === 3. 收益质量分析 ===
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print("\n\n" + "=" * 90)
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print(" 3. 持有期收益质量")
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print("=" * 90)
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for label, recs in [
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("原始 select=3", r1), ("IC校准 select=3", r2),
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("原始 select=1", r3), ("IC校准 select=1", r4),
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]:
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arr = np.array([r['daily_return'] for r in recs])
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win = arr[arr > 0]
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lose = arr[arr < 0]
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print(f"\n [{label}]:")
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print(f" 日均: {arr.mean()*100:+.4f}% 胜率: {(arr>0).mean()*100:.1f}%")
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print(f" 盈利均: {win.mean()*100:+.4f}% 亏损均: {lose.mean()*100:+.4f}% 盈亏比: {abs(win.mean()/lose.mean()):.3f}")
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# Worst 5 days
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sorted_rets = sorted(recs, key=lambda x: x['daily_return'])
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worst5 = [r['daily_return'] * 100 for r in sorted_rets[:5]]
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best5 = [r['daily_return'] * 100 for r in sorted_rets[-5:]]
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print(f" 最差5天: {['%.2f%%' % v for v in worst5]}")
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print(f" 最好5天: {['%.2f%%' % v for v in best5]}")
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# === 4. 分段绩效 ===
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print("\n\n" + "=" * 90)
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print(" 4. 分段绩效: 冷启动期(前170天) vs 校准生效期")
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print("=" * 90)
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for label, recs in [
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("原始 select=1", r3), ("IC校准 select=1", r4),
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("原始 select=3", r1), ("IC校准 select=3", r2),
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]:
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cold = recs[:170]
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warm = recs[170:]
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cold_rets = np.array([r['daily_return'] for r in cold])
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warm_rets = np.array([r['daily_return'] for r in warm])
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cold_cum = np.prod(1 + cold_rets) - 1
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warm_cum = np.prod(1 + warm_rets) - 1
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cold_ann = (1 + cold_cum) ** (252 / len(cold_rets)) - 1
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warm_ann = (1 + warm_cum) ** (252 / len(warm_rets)) - 1
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warm_nav = np.cumprod(1 + warm_rets)
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warm_peak = np.maximum.accumulate(warm_nav)
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warm_dd = (warm_nav - warm_peak) / warm_peak
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warm_maxdd = warm_dd.min()
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cold_nav = np.cumprod(1 + cold_rets)
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cold_peak = np.maximum.accumulate(cold_nav)
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cold_dd = (cold_nav - cold_peak) / cold_peak
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cold_maxdd = cold_dd.min()
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print(f"\n [{label}]:")
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print(f" 冷启动(1-170天): 累计={cold_cum*100:+.2f}% 年化={cold_ann*100:.2f}% 胜率={(cold_rets>0).mean()*100:.1f}% 最大回撤={cold_maxdd*100:.2f}%")
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print(f" 校准后(170天+): 累计={warm_cum*100:+.2f}% 年化={warm_ann*100:.2f}% 胜率={(warm_rets>0).mean()*100:.1f}% 最大回撤={warm_maxdd*100:.2f}%")
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# === 5. IC校准 select=3 退化的根因：新增持仓的收益质量 ===
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print("\n\n" + "=" * 90)
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print(" 5. IC校准 select=3: 新增持仓收益分析")
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print("=" * 90)
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# For each day, check if holdings changed between original and calibrated
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changed_days = 0
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new_asset_rets = []
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removed_asset_rets = []
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for orig_rec, calib_rec in zip(r1, r2):
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if orig_rec['date'] != calib_rec['date']:
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continue
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orig_set = set(orig_rec['holdings'])
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calib_set = set(calib_rec['holdings'])
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if orig_set != calib_set:
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changed_days += 1
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# The return of the calibrated portfolio on that day
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new_asset_rets.append(calib_rec['daily_return'])
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removed_asset_rets.append(orig_rec['daily_return'])
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print(f"\n 持仓变化天数: {changed_days} / {len(r1)}")
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if new_asset_rets:
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new_arr = np.array(new_asset_rets)
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old_arr = np.array(removed_asset_rets)
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diff = new_arr - old_arr
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print(f" 变化日 - IC校准收益: {new_arr.mean()*100:+.4f}% 原始收益: {old_arr.mean()*100:+.4f}% 差: {diff.mean()*100:+.4f}%")
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print(f" 变化日胜率: IC校准={((new_arr>0).mean()*100):.1f}% 原始={((old_arr>0).mean()*100):.1f}%")
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print(f" 变化日累计: IC校准={new_arr.sum()*100:+.2f}% 原始={old_arr.sum()*100:+.2f}% 差={diff.sum()*100:+.2f}%")
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# === 6. IC stats ===
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print("\n\n" + "=" * 90)
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print(" 6. IC校准最终状态")
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print("=" * 90)
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calibrator = s2.ic_calibrator
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for code in sorted(calibrator._history.keys()):
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stats = calibrator.get_stats(code)
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nm = s2.config.asset_pools.assets.get(code)
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name = nm.name if nm else code
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print(f" {name:<12} ({code:<14}): n={stats.n_samples:>5} IC={stats.ic_value:+.4f} sign={stats.ic_sign:+d}")
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