feat(analysis): report per-anno (Trade/Acc/DD/PnL) delle 3 strategie
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
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"""Report PER ANNO (Trade, Acc%, DD%, PnL%) delle 3 strategie oneste.
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Acc: DIP01/TR01 = win-rate dei trade chiusi (episodi); ROT01 = % giorni positivi.
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DD : drawdown massimo dell'equity DENTRO l'anno solare.
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PnL: variazione % dell'equity nell'anno (composta).
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Tutto NETTO (fee 0.10% RT, leva 3x, pos 15%). Replica gli engine di honest_*.
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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PROJECT_ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(PROJECT_ROOT))
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from scripts.analysis.honest_lab import atr, ema, get_df, available_assets, FEE_RT
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from scripts.analysis.honest_final import dip_entries
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from scripts.analysis.honest_rotation import build_panel
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LEV, POS = 3.0, 0.15
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def _yearly_dd(years: np.ndarray, equity: np.ndarray) -> dict[int, float]:
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"""DD massimo intra-anno da una serie di equity etichettata per anno."""
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out: dict[int, float] = {}
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for y in np.unique(years):
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eq = equity[years == y]
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peak = eq[0]; dd = 0.0
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for v in eq:
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peak = max(peak, v)
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dd = max(dd, (peak - v) / peak if peak > 0 else 0.0)
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out[int(y)] = dd * 100
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return out
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def _print(title, header, rows):
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print("\n" + "=" * 78)
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print(f" {title}")
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print("=" * 78)
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print(" " + header)
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print(" " + "-" * 74)
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for r in rows:
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print(" " + r)
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# --------------------------- DIP01 (trade-based) ---------------------------
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def dip_yearly(asset, tf="1h"):
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df = get_df(asset, tf)
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ents = dip_entries(df)
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h, l, c = df["high"].values, df["low"].values, df["close"].values
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n = len(c); ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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fee = FEE_RT * LEV
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cap = 1000.0
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last_exit = -1
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eq_y, eq_v = [], []
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yt: dict[int, list] = {} # year -> [trades, wins, pnl_start_cap, pnl_end_cap]
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for e in ents:
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i, d = e["i"], e["d"]
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if i <= last_exit or i + 1 >= n:
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continue
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entry = c[i]; tp, sl, mb = e["tp"], e["sl"], e["max_bars"]
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exit_p = c[min(i + mb, n - 1)]; j = min(i + mb, n - 1)
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for k in range(1, mb + 1):
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j = i + k
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if j >= n:
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j = n - 1; exit_p = c[j]; break
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if (d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl):
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exit_p = sl; break
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if (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp):
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exit_p = tp; break
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if k == mb:
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exit_p = c[j]
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ret = (exit_p - entry) / entry * d * LEV - fee
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cap = max(cap + cap * POS * ret, 10.0)
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last_exit = j
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y = ts.iloc[i].year
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rec = yt.setdefault(y, [0, 0, None, None])
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rec[0] += 1; rec[1] += ret > 0
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eq_y.append(y); eq_v.append(cap)
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dd = _yearly_dd(np.array(eq_y), np.array(eq_v))
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# PnL% anno: da equity prima/dopo
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rows = []
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prev = 1000.0
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yrs = sorted(yt)
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cum = {}
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cprev = 1000.0
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# ricostruisci equity di fine anno
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end_cap = {}
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for y, v in zip(eq_y, eq_v):
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end_cap[y] = v
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for y in yrs:
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t, w = yt[y][0], yt[y][1]
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ec = end_cap[y]
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pnl = (ec / cprev - 1) * 100
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cprev = ec
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rows.append(f"{y:>6d}{t:>8d}{(w/t*100 if t else 0):>8.1f}{dd.get(y,0):>8.1f}{pnl:>+10.1f}")
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return rows
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# --------------------------- TR01 (position episodes) ---------------------------
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def tr_yearly(asset, tf="4h", fast=20, slow=100):
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df = get_df(asset, tf)
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c = df["close"].values; n = len(c)
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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ef, es = ema(c, fast), ema(c, slow)
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sig = np.where(ef > es, 1.0, 0.0); sig[:slow] = 0.0
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cap = 1000.0; cur = 0.0
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fee = FEE_RT / 2 * LEV
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ep_start_cap = None; ep_year = None
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yt: dict[int, list] = {}
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eq_y, eq_v = [], []
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for i in range(n - 1):
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s = sig[i]
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if s != cur:
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cap -= cap * POS * fee * abs(s - cur)
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if s == 1.0: # apertura long
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ep_start_cap = cap; ep_year = ts.iloc[i].year
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elif cur == 1.0 and ep_start_cap is not None: # chiusura long
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rec = yt.setdefault(ep_year, [0, 0])
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rec[0] += 1; rec[1] += cap > ep_start_cap
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ep_start_cap = None
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cur = s
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pr = (c[i + 1] - c[i]) / c[i]
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cap = max(cap * (1 + POS * LEV * pr * cur), 10.0)
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eq_y.append(ts.iloc[i].year); eq_v.append(cap)
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if cur == 1.0 and ep_start_cap is not None:
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rec = yt.setdefault(ep_year, [0, 0]); rec[0] += 1; rec[1] += cap > ep_start_cap
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dd = _yearly_dd(np.array(eq_y), np.array(eq_v))
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end_cap = {}
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for y, v in zip(eq_y, eq_v):
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end_cap[y] = v
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rows = []; cprev = 1000.0
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for y in sorted(end_cap):
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t, w = yt.get(y, [0, 0])
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pnl = (end_cap[y] / cprev - 1) * 100; cprev = end_cap[y]
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rows.append(f"{y:>6d}{t:>8d}{(w/t*100 if t else 0):>8.1f}{dd.get(y,0):>8.1f}{pnl:>+10.1f}")
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return rows
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# --------------------------- ROT01 (daily portfolio) ---------------------------
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def rot_yearly(lookback=60, top_k=2, gross=0.45):
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panel = build_panel(available_assets(), "1d")
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P = panel.values; T, N = P.shape
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rets = np.zeros_like(P); rets[1:] = P[1:] / P[:-1] - 1
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years = panel.index.year.values
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cap = 1000.0; w = np.zeros(N)
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yt: dict[int, list] = {} # year -> [rebal, pos_days, days]
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eq_y, eq_v = [], []
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for i in range(lookback + 1, T - 1):
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mom = P[i] / P[i - lookback] - 1
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order = np.argsort(mom)[::-1]
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chosen = [j for j in order if mom[j] > 0][:top_k]
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new_w = np.zeros(N)
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for j in chosen:
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new_w[j] = gross / len(chosen)
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turnover = np.abs(new_w - w).sum()
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if turnover > 1e-9:
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cap -= cap * turnover * (FEE_RT / 2)
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w = new_w
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pr = float(np.dot(w, rets[i + 1]))
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cap = max(cap * (1 + pr), 10.0)
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y = int(years[i])
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rec = yt.setdefault(y, [0, 0, 0])
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rec[0] += turnover > 1e-9; rec[1] += pr > 0; rec[2] += 1
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eq_y.append(y); eq_v.append(cap)
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dd = _yearly_dd(np.array(eq_y), np.array(eq_v))
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end_cap = {}
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for y, v in zip(eq_y, eq_v):
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end_cap[y] = v
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rows = []; cprev = 1000.0
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for y in sorted(end_cap):
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reb, pos, days = yt[y]
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pnl = (end_cap[y] / cprev - 1) * 100; cprev = end_cap[y]
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rows.append(f"{y:>6d}{reb:>8d}{(pos/days*100 if days else 0):>8.1f}{dd.get(y,0):>8.1f}{pnl:>+10.1f}")
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return rows
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if __name__ == "__main__":
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H = f"{'Anno':>6s}{'Trade':>8s}{'Acc%':>8s}{'DD%':>8s}{'PnL%':>10s}"
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for a in ["BTC", "ETH", "SOL"]:
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_print(f"DIP01 — {a} 1h (Acc = win-rate trade)", H, dip_yearly(a))
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for a in ["BNB", "BTC", "DOGE", "SOL", "XRP"]:
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_print(f"TR01 — {a} 4h (Trade = episodi long, Acc = win-rate episodi)", H, tr_yearly(a))
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_print("ROT01 — paniere 8 crypto 1d (Trade = ribilanciamenti, Acc = % giorni positivi)",
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H, rot_yearly())
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