zhutoutoutousan
605faf5310
Add cluster audit pipeline, united EA updates, brochure generators, and publication hygiene (gitignore, MT5 path desensitization, pre-upload scan). Remove tracked reports, models, and binary artifacts from the repo. Co-authored-by: Cursor <cursoragent@cursor.com>
410 lines
15 KiB
Python
410 lines
15 KiB
Python
"""
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SimpleEMA v3 — trend pullback engine (shared by main.mq5 mirror + optimizer).
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Logic:
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1. H4 EMA defines bias (long above / short below)
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2. M15 slow EMA slope confirms trend
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3. Entry on fast-EMA pullback + optional bullish/bearish bar
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4. ADX + DI filter; ATR band skips chop / spikes
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5. Breakeven after BE trigger; optional ATR trail after BE
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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import MetaTrader5 as mt5
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import numpy as np
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import pandas as pd
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from indicator_utils import calculate_adx, calculate_atr, calculate_dmi, calculate_ema # noqa: E402
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@dataclass
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class V3Params:
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fast_ema: int = 8
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slow_ema: int = 34
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min_ema_gap_pips: float = 1.0
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cooldown_bars: int = 4
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atr_period: int = 14
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atr_sl_mult: float = 2.0
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atr_tp_mult: float = 4.5
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max_bars_in_trade: int = 72
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htf_ema_period: int = 100
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adx_period: int = 14
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adx_min: float = 18.0
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adx_max: float = 42.0
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min_atr_pips: float = 4.0
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max_atr_pips: float = 22.0
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slope_lookback: int = 5
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require_bullish_bar: bool = True
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use_di_filter: bool = True
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use_breakeven: bool = True
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be_trigger_atr: float = 1.0
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be_offset_pips: float = 1.0
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use_trail_after_be: bool = True
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trail_atr_mult: float = 1.2
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session_start: int = 7
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session_end: int = 21
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max_spread_pips: float = 8.0
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lot_size: float = 0.10
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initial_balance: float = 10_000.0
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@dataclass
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class V3Market:
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df: pd.DataFrame
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open_: np.ndarray
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high: np.ndarray
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low: np.ndarray
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close: np.ndarray
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hours: np.ndarray
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fast: np.ndarray
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slow: np.ndarray
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atr: np.ndarray
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adx: np.ndarray
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plus_di: np.ndarray
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minus_di: np.ndarray
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htf: np.ndarray
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@dataclass
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class V3Result:
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net_profit: float
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total_trades: int
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win_rate: float
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profit_factor: float
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max_drawdown_pct: float
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sharpe: float
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trades: list[dict]
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@dataclass
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class V3Cache:
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df: pd.DataFrame
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open_: np.ndarray
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high: np.ndarray
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low: np.ndarray
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close: np.ndarray
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hours: np.ndarray
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fast: dict[int, np.ndarray]
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slow: dict[int, np.ndarray]
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atr: dict[int, np.ndarray]
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adx: dict[int, np.ndarray]
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plus_di: dict[int, np.ndarray]
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minus_di: dict[int, np.ndarray]
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htf: dict[int, np.ndarray]
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def load_v3_cache(df: pd.DataFrame) -> V3Cache:
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close_s = df["close"]
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h4 = close_s.resample("4h").last().dropna()
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return V3Cache(
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df=df,
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open_=df["open"].to_numpy(),
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high=df["high"].to_numpy(),
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low=df["low"].to_numpy(),
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close=close_s.to_numpy(),
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hours=df.index.hour.to_numpy(),
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fast={p: calculate_ema(close_s, p).to_numpy() for p in range(6, 13)},
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slow={p: calculate_ema(close_s, p).to_numpy() for p in range(26, 53, 2)},
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atr={p: calculate_atr(df, p).to_numpy() for p in (10, 14, 20)},
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adx={p: calculate_adx(df, p).to_numpy() for p in (10, 14, 20)},
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plus_di={p: calculate_dmi(df, p)["plus_di"].to_numpy() for p in (10, 14, 20)},
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minus_di={p: calculate_dmi(df, p)["minus_di"].to_numpy() for p in (10, 14, 20)},
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htf={p: calculate_ema(h4, p).reindex(df.index, method="ffill").to_numpy() for p in (50, 100, 200)},
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)
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def market_from_cache(cache: V3Cache, p: V3Params) -> V3Market:
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return V3Market(
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df=cache.df,
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open_=cache.open_,
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high=cache.high,
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low=cache.low,
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close=cache.close,
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hours=cache.hours,
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fast=cache.fast[p.fast_ema],
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slow=cache.slow[p.slow_ema],
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atr=cache.atr[p.atr_period],
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adx=cache.adx[p.adx_period],
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plus_di=cache.plus_di[p.adx_period],
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minus_di=cache.minus_di[p.adx_period],
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htf=cache.htf[p.htf_ema_period],
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)
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def load_v3_market(df: pd.DataFrame, p: V3Params) -> V3Market:
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close_s = df["close"]
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h4 = close_s.resample("4h").last().dropna()
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dmi = calculate_dmi(df, p.adx_period)
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return V3Market(
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df=df,
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open_=df["open"].to_numpy(),
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high=df["high"].to_numpy(),
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low=df["low"].to_numpy(),
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close=close_s.to_numpy(),
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hours=df.index.hour.to_numpy(),
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fast=calculate_ema(close_s, p.fast_ema).to_numpy(),
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slow=calculate_ema(close_s, p.slow_ema).to_numpy(),
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atr=calculate_atr(df, p.atr_period).to_numpy(),
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adx=calculate_adx(df, p.adx_period).to_numpy(),
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plus_di=dmi["plus_di"].to_numpy(),
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minus_di=dmi["minus_di"].to_numpy(),
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htf=calculate_ema(h4, p.htf_ema_period).reindex(df.index, method="ffill").to_numpy(),
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)
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def _session_ok(hours: np.ndarray, start: int, end: int) -> np.ndarray:
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if start <= 0 and end >= 24:
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return np.ones(len(hours), dtype=bool)
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if start < end:
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return (hours >= start) & (hours < end)
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return (hours >= start) | (hours < end)
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def build_v3_signals(md: V3Market, p: V3Params, pip: float) -> tuple[np.ndarray, np.ndarray]:
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n = len(md.close)
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lb = max(p.slope_lookback, 1)
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slow_slope_up = md.slow > np.roll(md.slow, lb)
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slow_slope_dn = md.slow < np.roll(md.slow, lb)
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c1 = np.roll(md.close, 1)
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o1 = np.roll(md.open_, 1)
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h1 = np.roll(md.high, 1)
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l1 = np.roll(md.low, 1)
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f1 = np.roll(md.fast, 1)
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s1 = np.roll(md.slow, 1)
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htf1 = np.roll(md.htf, 1)
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adx1 = np.roll(md.adx, 1)
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pdi1 = np.roll(md.plus_di, 1)
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mdi1 = np.roll(md.minus_di, 1)
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atr1 = np.roll(md.atr, 1)
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atr_pips = atr1 / pip
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gap_ok = np.abs(f1 - s1) / pip >= p.min_ema_gap_pips
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atr_ok = (atr_pips >= p.min_atr_pips) & (atr_pips <= p.max_atr_pips)
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adx_ok = (adx1 >= p.adx_min) & (adx1 <= p.adx_max)
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sess = _session_ok(np.roll(md.hours, 1), p.session_start, p.session_end)
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bull_bar = (c1 > o1) if p.require_bullish_bar else np.ones(n, dtype=bool)
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bear_bar = (c1 < o1) if p.require_bullish_bar else np.ones(n, dtype=bool)
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di_long = (pdi1 > mdi1) if p.use_di_filter else np.ones(n, dtype=bool)
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di_short = (mdi1 > pdi1) if p.use_di_filter else np.ones(n, dtype=bool)
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long_trend = (f1 > s1) & (c1 > htf1) & slow_slope_up & (c1 > s1)
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short_trend = (f1 < s1) & (c1 < htf1) & slow_slope_dn & (c1 < s1)
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pullback_long = long_trend & (l1 <= f1) & (c1 > f1) & bull_bar
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pullback_short = short_trend & (h1 >= f1) & (c1 < f1) & bear_bar
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buy = pullback_long & gap_ok & atr_ok & adx_ok & sess & di_long
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sell = pullback_short & gap_ok & atr_ok & adx_ok & sess & di_short
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warm = max(p.slow_ema + lb + 3, 30)
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buy[:warm] = False
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sell[:warm] = False
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return buy, sell
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def simulate_v3(md: V3Market, symbol: str, p: V3Params, costs, pip: float, point: float) -> V3Result:
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buy_sig, sell_sig = build_v3_signals(md, p, pip)
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opn, high, low, close, atr = md.open_, md.high, md.low, md.close, md.atr
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spread_px = costs.spread_points * point
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slip = costs.slippage_points * point
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half = spread_px / 2.0 + slip
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commission = costs.commission_per_lot * p.lot_size * 2.0
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be_off = p.be_offset_pips * pip
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balance = p.initial_balance
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equity = [balance]
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trades: list[dict] = []
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side = None
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entry = 0.0
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entry_i = 0
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sl_px = 0.0
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tp_px = 0.0
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trail = 0.0
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be_active = False
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last_entry_i = -10_000
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def calc_profit(entry_px: float, exit_px: float, s: str) -> float:
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ot = mt5.ORDER_TYPE_BUY if s == "BUY" else mt5.ORDER_TYPE_SELL
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pr = mt5.order_calc_profit(ot, symbol, p.lot_size, entry_px, exit_px)
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return float(pr) - commission if pr is not None else -commission
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warm = max(p.slow_ema + p.slope_lookback + 5, 30)
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for i in range(warm, len(md.df)):
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atr1 = float(atr[i - 1]) if not np.isnan(atr[i - 1]) else 0.0
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mid = float(opn[i])
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if side is not None:
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closed = False
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bars_held = i - entry_i
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if p.max_bars_in_trade > 0 and bars_held >= p.max_bars_in_trade:
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xp = mid - half if side == "BUY" else mid + half
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pr = calc_profit(entry, xp, side)
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balance += pr
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trades.append({"side": side, "open_i": entry_i, "close_i": i, "profit": pr, "exit_reason": "max_bars"})
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closed = True
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if not closed and side == "BUY":
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if p.use_breakeven and not be_active and atr1 > 0 and high[i] >= entry + atr1 * p.be_trigger_atr:
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be_active = True
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sl_px = max(sl_px, entry + be_off)
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if be_active and p.use_trail_after_be and atr1 > 0:
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cand = high[i] - atr1 * p.trail_atr_mult
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if cand > entry:
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trail = max(trail, cand) if trail > 0 else cand
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sl_px = max(sl_px, trail)
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eff_sl = sl_px
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if low[i] <= eff_sl:
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reason = "trail" if trail > 0 and eff_sl > entry + be_off else ("be" if be_active and eff_sl >= entry else "sl")
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pr = calc_profit(entry, eff_sl - half, side)
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balance += pr
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trades.append({"side": side, "open_i": entry_i, "close_i": i, "profit": pr, "exit_reason": reason})
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closed = True
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elif high[i] >= tp_px:
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pr = calc_profit(entry, tp_px - half, side)
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balance += pr
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trades.append({"side": side, "open_i": entry_i, "close_i": i, "profit": pr, "exit_reason": "tp"})
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closed = True
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elif not closed and side == "SELL":
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if p.use_breakeven and not be_active and atr1 > 0 and low[i] <= entry - atr1 * p.be_trigger_atr:
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be_active = True
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sl_px = min(sl_px, entry - be_off)
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if be_active and p.use_trail_after_be and atr1 > 0:
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cand = low[i] + atr1 * p.trail_atr_mult
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if cand < entry:
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trail = min(trail, cand) if trail > 0 else cand
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sl_px = min(sl_px, trail)
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eff_sl = sl_px
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if high[i] >= eff_sl:
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reason = "trail" if trail > 0 and eff_sl < entry - be_off else ("be" if be_active and eff_sl <= entry else "sl")
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pr = calc_profit(entry, eff_sl + half, side)
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balance += pr
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trades.append({"side": side, "open_i": entry_i, "close_i": i, "profit": pr, "exit_reason": reason})
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closed = True
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elif low[i] <= tp_px:
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pr = calc_profit(entry, tp_px + half, side)
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balance += pr
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trades.append({"side": side, "open_i": entry_i, "close_i": i, "profit": pr, "exit_reason": "tp"})
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closed = True
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if closed:
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side = None
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be_active = False
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trail = 0.0
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if side is None:
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spread_pips = spread_px / pip if pip > 0 else 0
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if (p.max_spread_pips <= 0 or spread_pips <= p.max_spread_pips) and i - last_entry_i >= p.cooldown_bars:
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if buy_sig[i] and atr1 > 0:
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side = "BUY"
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entry = mid + half
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entry_i = i
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sl_px = entry - atr1 * p.atr_sl_mult
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tp_px = entry + atr1 * p.atr_tp_mult
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be_active = False
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trail = 0.0
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last_entry_i = i
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elif sell_sig[i] and atr1 > 0:
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side = "SELL"
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entry = mid - half
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entry_i = i
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sl_px = entry + atr1 * p.atr_sl_mult
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tp_px = entry - atr1 * p.atr_tp_mult
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be_active = False
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trail = 0.0
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last_entry_i = i
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mark = balance
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if side == "BUY":
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mark += calc_profit(entry, float(close[i - 1]), side) + commission
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elif side == "SELL":
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mark += calc_profit(entry, float(close[i - 1]), side) + commission
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equity.append(mark)
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if side is not None:
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pr = calc_profit(entry, float(close[-1]), side)
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balance += pr
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trades.append({"side": side, "open_i": entry_i, "close_i": len(md.df) - 1, "profit": pr, "exit_reason": "eod"})
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eq = pd.Series(equity[: len(md.df)], index=md.df.index[: len(equity)])
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net = balance - p.initial_balance
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wins = [t["profit"] for t in trades if t["profit"] > 0]
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losses = [t["profit"] for t in trades if t["profit"] <= 0]
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gp = sum(wins) if wins else 0.0
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gl = abs(sum(losses)) if losses else 0.0
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pf = gp / gl if gl > 0 else 0.0
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wr = 100.0 * len(wins) / len(trades) if trades else 0.0
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dd = abs(float(((eq - eq.cummax()) / eq.cummax() * 100).min())) if len(eq) else 0.0
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rets = eq.pct_change().dropna()
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sharpe = float(rets.mean() / rets.std() * np.sqrt(252 * 24 * 4)) if len(rets) > 1 and rets.std() > 0 else 0.0
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return V3Result(net, len(trades), wr, pf, dd, sharpe, trades)
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def sample_v3(rng) -> V3Params:
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return V3Params(
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fast_ema=rng.randint(6, 12),
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slow_ema=rng.choice([p for p in range(28, 51, 2)]),
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min_ema_gap_pips=round(rng.uniform(0.5, 3.0), 1),
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cooldown_bars=rng.choice([3, 4, 5, 6]),
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atr_period=rng.choice([10, 14, 20]),
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atr_sl_mult=round(rng.uniform(1.6, 2.8), 2),
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atr_tp_mult=round(rng.uniform(3.5, 6.5), 2),
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max_bars_in_trade=rng.choice([48, 72, 96]),
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htf_ema_period=rng.choice([50, 100, 200]),
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adx_min=round(rng.uniform(16, 24), 1),
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adx_max=round(rng.uniform(35, 50), 1),
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min_atr_pips=round(rng.uniform(2.0, 6.0), 1),
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max_atr_pips=round(rng.uniform(15, 30), 1),
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slope_lookback=rng.choice([4, 5, 6, 8]),
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require_bullish_bar=rng.choice([True, True, False]),
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use_di_filter=rng.choice([True, True, False]),
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use_breakeven=rng.choice([True, True, False]),
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be_trigger_atr=round(rng.uniform(0.8, 1.5), 2),
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use_trail_after_be=rng.choice([True, False]),
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trail_atr_mult=round(rng.uniform(1.0, 1.8), 2),
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session_start=rng.choice([6, 7, 8]),
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session_end=rng.choice([20, 21, 22]),
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max_spread_pips=rng.choice([6, 8]),
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)
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def write_v3_set(p: V3Params, path) -> None:
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lines = [
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"; SimpleEMA v3 — trend pullback",
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"Timeframe=16388",
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f"FastEmaPeriod={p.fast_ema}",
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f"SlowEmaPeriod={p.slow_ema}",
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f"MinEmaGapPips={p.min_ema_gap_pips}",
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f"CooldownBars={p.cooldown_bars}",
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f"AtrPeriod={p.atr_period}",
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f"AtrSlMult={p.atr_sl_mult}",
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f"AtrTpMult={p.atr_tp_mult}",
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f"MaxBarsInTrade={p.max_bars_in_trade}",
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f"HtfEmaPeriod={p.htf_ema_period}",
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f"AdxPeriod={p.adx_period}",
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f"AdxMin={p.adx_min}",
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f"AdxMax={p.adx_max}",
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f"MinAtrPips={p.min_atr_pips}",
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f"MaxAtrPips={p.max_atr_pips}",
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f"SlopeLookback={p.slope_lookback}",
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f"RequireBullishBar={'true' if p.require_bullish_bar else 'false'}",
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f"UseDiFilter={'true' if p.use_di_filter else 'false'}",
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f"UseBreakeven={'true' if p.use_breakeven else 'false'}",
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f"BeTriggerAtr={p.be_trigger_atr}",
|
|
f"BeOffsetPips={p.be_offset_pips}",
|
|
f"UseTrailAfterBe={'true' if p.use_trail_after_be else 'false'}",
|
|
f"TrailAtrMult={p.trail_atr_mult}",
|
|
f"SessionStartHour={p.session_start}",
|
|
f"SessionEndHour={p.session_end}",
|
|
f"MaxSpreadPips={p.max_spread_pips}",
|
|
f"LotSize={p.lot_size}",
|
|
]
|
|
path.write_text("\n".join(lines) + "\n", encoding="utf-8")
|