55 lines
2.1 KiB
Python
55 lines
2.1 KiB
Python
"""DIP01 — Dip-buy mean-reversion single-asset (z-score sotto-banda). Honest family.
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Replica live della logica validata in scripts/analysis/honest_improve2.dip_market_gated
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(con market_n=0, come lo sleeve DIP01_BTC del portafoglio): compra quando lo z-score del
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prezzo rispetto a SMA(n) incrocia sotto -z_in; esce a TP=SMA, SL=close-sl_atr*ATR, o max_bars.
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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 src.strategies.base import Strategy, Signal # noqa: E402
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def _atr(df, n=14):
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h, l, c = df["high"].values, df["low"].values, df["close"].values
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pc = np.roll(c, 1); pc[0] = c[0]
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tr = np.maximum(h - l, np.maximum(np.abs(h - pc), np.abs(l - pc)))
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return pd.Series(tr).rolling(n).mean().values
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class Dip01DipBuy(Strategy):
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name = "DIP01_dip_buy"
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description = "Dip-buy mean-reversion single-asset (z-score), exit TP=SMA/SL=ATR/max_bars"
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default_assets = ["BTC"]
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default_timeframes = ["1h"]
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fee_rt = 0.001
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leverage = 3.0
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position_size = 0.15
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def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
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n: int = 50, z_in: float = 2.5, sl_atr: float = 2.5,
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max_bars: int = 24, **params) -> list[Signal]:
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c = df["close"].values
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ma = pd.Series(c).rolling(n).mean().values
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sd = pd.Series(c).rolling(n).std().values
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a = _atr(df, 14)
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z = (c - ma) / np.where(sd == 0, np.nan, sd)
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out: list[Signal] = []
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for i in range(n + 14, len(c)):
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if np.isnan(z[i]) or np.isnan(a[i]) or np.isnan(ma[i]):
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continue
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if z[i] <= -z_in and z[i - 1] > -z_in:
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out.append(Signal(idx=i, direction=1, entry_price=float(c[i]),
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metadata={"tp": float(ma[i]),
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"sl": float(c[i] - sl_atr * a[i]),
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"max_bars": int(max_bars)}))
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return out
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