Pietro GiacobazziandDevin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
19c2e0ac72
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
"""Fase 2 raffinata: extra-rendimento vs baseline + significativita'.
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Per ogni (coppia, direzione) calcola:
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- rendimento medio grezzo a 1/3/5/10/20 g
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- EXTRA-rendimento = evento - baseline nello stesso regime (toglie il bias di periodo)
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- effetto in sigma del movimento tipico a h giorni
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- quota di eventi che battono la baseline
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- p-value (block bootstrap) e q-value (Benjamini-Hochberg)
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Uso:
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python excess_analysis.py [SYMBOL] [YEAR_MIN]
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Output:
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../results/summary_<SYM>_<YEAR>.csv + stampa dei top a 5/10/20 g
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"""
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import os, sys, numpy as np, pandas as pd
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from _common import load_pair, add_regime, regime_edges, block_bootstrap_p, benjamini_hochberg, HZ, HERE
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SYM = sys.argv[1] if len(sys.argv) > 1 else "EURUSD"
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YEAR = int(sys.argv[2]) if len(sys.argv) > 2 else 1999
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RESULTS = os.path.normpath(os.path.join(HERE, "..", "results"))
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cr, ba = load_pair(SYM, YEAR)
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print(f"[{SYM} {YEAR}+] incroci={len(cr)} baseline={len(ba)}")
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edges = regime_edges(ba)
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cr = add_regime(cr, edges)
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ba = add_regime(ba, edges)
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base_mean = {h: ba.groupby("regime")[f"cret_{h}"].mean() for h in HZ}
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base_glob = {h: ba[f"cret_{h}"].mean() for h in HZ}
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base_sd = {h: ba[f"cret_{h}"].std() for h in HZ}
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rows = []
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for (pair, d), g in cr.groupby(["pair", "dir"]):
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n = len(g)
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if n < 30:
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continue
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rec = {"pair": pair, "dir": int(d), "n": n}
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for h in HZ:
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ev = g[f"cret_{h}"].values
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bexp = g["regime"].map(base_mean[h]).fillna(base_glob[h]).values
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exc = ev - bexp
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rec[f"raw_{h}"] = np.nanmean(ev)
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rec[f"exc_{h}"] = np.nanmean(exc)
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rec[f"eff_{h}"] = np.nanmean(exc) / base_sd[h]
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rec[f"pos_{h}"] = (exc > 0).mean()
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rec[f"p_{h}"] = block_bootstrap_p(exc)
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rows.append(rec)
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res = pd.DataFrame(rows)
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for h in HZ:
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res[f"q_{h}"] = benjamini_hochberg(res[f"p_{h}"].values)
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os.makedirs(RESULTS, exist_ok=True)
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outp = os.path.join(RESULTS, f"summary_{SYM}_{YEAR}.csv")
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res.to_csv(outp, index=False)
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print(f"vol giornaliera prezzo (sd cret_1) = {ba['cret_1'].std():.3f}%")
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print(f"baseline drift 5/10/20g = {base_glob[5]:.3f}% / {base_glob[10]:.3f}% / {base_glob[20]:.3f}%")
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for h in [5, 10, 20]:
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sig = res[res[f"q_{h}"] < 0.10].copy()
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sig["ae"] = sig[f"eff_{h}"].abs()
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sig = sig.sort_values("ae", ascending=False).head(12)
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print(f"\n=== TOP {h}g (q<0.10) ===")
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for _, r in sig.iterrows():
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print(f" {r['pair']:13s} dir={int(r['dir']):+d} n={int(r['n']):5d} "
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f"exc={r[f'exc_{h}']:+.3f}% eff={r[f'eff_{h}']:+.2f}sd "
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f"pos={r[f'pos_{h}']*100:.0f}% q={r[f'q_{h}']:.3f}")
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print(f"\nsalvato: {outp}")
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