PythagorasGoal/scripts/live/paper_prevday.py

"""FORWARD-MONITOR — PREVDAY RANGE BREAKOUT (lead ortogonale a TP01), forward-only, PAPER.

NON è esecuzione reale. È il monitoraggio forward-only del LEAD validato dall'onda intraday
(src/strategies/prevday_breakout.py, parametri CONGELATI) per vedere se l'edge in-sample regge
FUORI CAMPIONE VERO nei prossimi mesi. Stesso trattamento di XS01 STAT-MODE / STA05.

DESIGN (onesto):
  - Legge i parquet certificati BTC/ETH 1h (data/raw). Segnale a 1h, libro 50/50.
  - Alla prima esecuzione parte dall'ultima barra 1h CHIUSA (forward-only: lo storico NON entra
    nel PnL di paper, si traccia solo da ora in avanti).
  - Ogni run processa le NUOVE barre 1h chiuse: applica il rendimento della posizione tenuta,
    addebita le fee sul turnover, registra i flip di segno, poi ricalcola la posizione-bersaglio.
  - Traccia DUE libri in parallelo per onestà sull'esecuzione (lo scettico ha segnalato che a $600
    il micro-ribilanciamento del vol-target ha un haircut di fill):
      * MODELED  : capitale nominale $2000, ribilanciamento continuo (fee proporzionale su ogni |Δ|).
      * REAL-$600: capitale reale $600, salta i ribilanciamenti di nozionale < min_order ($5) —
        cosa che il conto vero catturerebbe davvero. Il gap MODELED-REAL = l'haircut di fill reale.
  - Per barre fresche, aggiornare prima i dati:
        uv run python scripts/analysis/rebuild_history.py --asset BTC ETH

Stato: data/paper_prevday/{state.json, trades.jsonl, returns.jsonl} (append-only).

    uv run python scripts/live/paper_prevday.py            # avanza col dato disponibile
    uv run python scripts/live/paper_prevday.py --status    # solo stato, non avanza
    uv run python scripts/live/paper_prevday.py --reset      # azzera (riparte da ora)
"""
from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path

import numpy as np
import pandas as pd

PROJECT_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(PROJECT_ROOT))

from src.backtest.harness import load                                   # noqa: E402
from src.strategies.prevday_breakout import target as prevday_target    # noqa: E402
from src.strategies import prevday_breakout as pb                       # noqa: E402

STATE_DIR = PROJECT_ROOT / "data" / "paper_prevday"
STATE_FILE = STATE_DIR / "state.json"
TRADES_FILE = STATE_DIR / "trades.jsonl"
RETURNS_FILE = STATE_DIR / "returns.jsonl"
ASSETS = ["BTC", "ETH"]
WEIGHT = 0.5
FEE_SIDE = 0.0005              # 0.05%/side = 0.10% round-trip (Deribit taker)
MODELED_CAPITAL = 2000.0       # nominale, ribilanciamento continuo
REAL_CAPITAL = 600.0           # capitale mainnet reale
MIN_ORDER = 5.0                # min order Deribit -> sotto, il conto vero NON ribilancia


def build_bars() -> dict[str, pd.DataFrame]:
    return {a: load(a, "1h").reset_index(drop=True) for a in ASSETS}


def _state_io(write: dict | None = None):
    if write is not None:
        STATE_DIR.mkdir(parents=True, exist_ok=True)
        STATE_FILE.write_text(json.dumps(write, indent=2))
        return write
    return json.loads(STATE_FILE.read_text()) if STATE_FILE.exists() else None


def _append(path: Path, rec: dict):
    STATE_DIR.mkdir(parents=True, exist_ok=True)
    with open(path, "a") as f:
        f.write(json.dumps(rec) + "\n")


def init_state(dfs) -> dict:
    last_ts = min(int(dfs[a]["timestamp"].iloc[-1]) for a in ASSETS)
    pos = {a: pb.current_target(dfs[a][dfs[a]["timestamp"] <= last_ts]) for a in ASSETS}
    return dict(
        start_ts=last_ts, last_ts=last_ts, n_bars=0,
        pos_modeled=pos, pos_real=dict(pos),
        cap_modeled=MODELED_CAPITAL, cap_real=REAL_CAPITAL,
        peak_modeled=MODELED_CAPITAL, peak_real=REAL_CAPITAL,
        dd_modeled=0.0, dd_real=0.0, n_trades=0,
    )


def advance(st: dict, dfs: dict) -> dict:
    data = {}
    for a in ASSETS:
        df = dfs[a]
        c = df["close"].values.astype(float)
        r = np.zeros(len(c)); r[1:] = c[1:] / c[:-1] - 1.0
        data[a] = dict(ts=df["timestamp"].values.astype("int64"),
                       dt=pd.to_datetime(df["datetime"]).values, r=r,
                       tgt=prevday_target(df))
    common = sorted(set(data["BTC"]["ts"]).intersection(data["ETH"]["ts"]))
    new_ts = [t for t in common if t > st["last_ts"]]
    if not new_ts:
        return st
    idx = {a: {int(t): i for i, t in enumerate(data[a]["ts"])} for a in ASSETS}
    pm, pr = dict(st["pos_modeled"]), dict(st["pos_real"])
    cm, cr = st["cap_modeled"], st["cap_real"]
    pkm, pkr = st["peak_modeled"], st["peak_real"]
    ddm, ddr = st["dd_modeled"], st["dd_real"]
    ntr = st.get("n_trades", 0)

    for t in new_ts:
        net_m = net_r = 0.0
        nm, nr = {}, {}
        for a in ASSETS:
            i = idx[a][int(t)]
            r = float(data[a]["r"][i]); tgt = float(data[a]["tgt"][i])
            # MODELED: continuous rebalance
            hm = pm[a]
            net_m += WEIGHT * (hm * r - FEE_SIDE * abs(tgt - hm))
            nm[a] = tgt
            if np.sign(tgt) != np.sign(hm):
                _append(TRADES_FILE, dict(ts=int(t), dt=str(pd.Timestamp(data[a]["dt"][i])),
                        asset=a, action="ENTRY" if tgt != 0 else "EXIT",
                        from_pos=round(hm, 4), to_pos=round(tgt, 4)))
                ntr += 1
            # REAL-$600: skip sub-min_order rebalances
            hr = pr[a]
            leg_cap = cr * WEIGHT
            executed = abs(tgt - hr) * leg_cap >= MIN_ORDER
            new_hr = tgt if executed else hr
            net_r += WEIGHT * (hr * r - FEE_SIDE * abs(new_hr - hr))
            nr[a] = new_hr
        cm *= (1.0 + max(net_m, -0.99)); cr *= (1.0 + max(net_r, -0.99))
        pkm = max(pkm, cm); pkr = max(pkr, cr)
        ddm = max(ddm, (pkm - cm) / pkm if pkm > 0 else 0.0)
        ddr = max(ddr, (pkr - cr) / pkr if pkr > 0 else 0.0)
        pm, pr = nm, nr
        _append(RETURNS_FILE, dict(ts=int(t), dt=str(pd.Timestamp(data["BTC"]["dt"][idx["BTC"][int(t)]])),
                net_modeled=round(net_m, 6), net_real=round(net_r, 6),
                pos_btc=round(pr["BTC"], 4), pos_eth=round(pr["ETH"], 4),
                cap_modeled=round(cm, 2), cap_real=round(cr, 2)))

    st.update(last_ts=int(new_ts[-1]), n_bars=st.get("n_bars", 0) + len(new_ts),
              pos_modeled=pm, pos_real=pr, cap_modeled=cm, cap_real=cr,
              peak_modeled=pkm, peak_real=pkr, dd_modeled=ddm, dd_real=ddr, n_trades=ntr)
    return st


def print_status(st: dict, dfs: dict):
    days = (max(int(dfs[a]["timestamp"].iloc[-1]) for a in ASSETS) - st["start_ts"]) / 86400_000
    rm = st["cap_modeled"] / MODELED_CAPITAL - 1
    rr = st["cap_real"] / REAL_CAPITAL - 1
    print(f"\n  PREVDAY-BREAKOUT forward-monitor (PAPER, lead ortogonale a TP01 — non deploy)")
    print(f"  forward da {pd.Timestamp(st['start_ts'], unit='ms', tz='UTC').date()}  "
          f"({st['n_bars']} barre 1h ~{days:.0f}g)   trade(flip): {st['n_trades']}")
    print(f"  posizione corrente: BTC {st['pos_real']['BTC']:+.3f}  ETH {st['pos_real']['ETH']:+.3f}")
    print(f"  MODELED  ($2000 nominale): {rm*100:+6.2f}%   eq ${st['cap_modeled']:.2f}   maxDD {st['dd_modeled']*100:.1f}%")
    print(f"  REAL-$600 (min-order $5) : {rr*100:+6.2f}%   eq ${st['cap_real']:.2f}   maxDD {st['dd_real']*100:.1f}%")
    print(f"  -> fill-haircut MODELED-REAL: {(rm-rr)*100:+.2f} pp  (lo scettico l'aveva segnalato)")
    print(f"  log: {RETURNS_FILE}\n")


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--status", action="store_true")
    ap.add_argument("--reset", action="store_true")
    args = ap.parse_args()
    dfs = build_bars()
    if args.reset:
        for p in (STATE_FILE, TRADES_FILE, RETURNS_FILE):
            if p.exists():
                p.unlink()
        st = init_state(dfs); _state_io(st)
        print("forward-monitor inizializzato (forward-only da ora).")
        print_status(st, dfs); return
    st = _state_io()
    if st is None:
        st = init_state(dfs); _state_io(st)
        print("forward-monitor inizializzato (forward-only da ora).")
        print_status(st, dfs); return
    if not args.status:
        st = advance(st, dfs); _state_io(st)
    print_status(st, dfs)


if __name__ == "__main__":
    main()