Prepare source-only public release for develop.
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>
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zhutoutoutousan
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Cursor
Cursor
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@@ -0,0 +1,477 @@
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"""
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SimpleEMA v4 — regime-aware dual entry + partial take-profit.
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Changes vs v3:
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1. Chop filter: skip when fast/slow crossed too often recently
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2. Trend quality: ADX rising + EMA gap scaled by ATR (not fixed pips)
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3. Dual entry: EMA cross OR deep pullback in established trend
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4. Partial TP: scale out at TP1, trail remainder toward TP2
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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 V4Params:
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fast_ema: int = 10
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slow_ema: int = 42
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entry_mode: int = 1 # 0=cross, 1=cross+pullback, 2=pullback
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min_ema_gap_atr: float = 0.12
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chop_lookback: int = 24
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max_chop_crosses: int = 1
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pullback_swing_bars: int = 12
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pullback_min_depth_atr: float = 0.35
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adx_rising_bars: int = 3
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cooldown_bars: int = 5
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atr_period: int = 14
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atr_sl_mult: float = 2.2
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tp1_atr_mult: float = 1.8
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tp1_close_pct: float = 0.5
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tp2_atr_mult: float = 5.5
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max_bars_in_trade: int = 80
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htf_ema_period: int = 100
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adx_period: int = 14
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adx_min: float = 20.0
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adx_max: float = 45.0
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min_atr_pips: float = 3.0
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max_atr_pips: float = 24.0
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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_partial_tp: bool = True
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use_trail_after_tp1: bool = True
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trail_atr_mult: float = 1.4
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be_offset_pips: float = 1.0
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session_start: int = 8
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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 V4Market:
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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 V4Result:
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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 V4Cache:
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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_v4_cache(df: pd.DataFrame) -> V4Cache:
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close_s = df["close"]
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h4 = close_s.resample("4h").last().dropna()
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return V4Cache(
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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, 14)},
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slow={p: calculate_ema(close_s, p).to_numpy() for p in range(28, 55, 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: V4Cache, p: V4Params) -> V4Market:
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return V4Market(
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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 _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 _rolling_cross_count(fast: np.ndarray, slow: np.ndarray, lookback: int) -> np.ndarray:
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n = len(fast)
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f1, f2 = np.roll(fast, 1), np.roll(fast, 2)
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s1, s2 = np.roll(slow, 1), np.roll(slow, 2)
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cross = ((f2 <= s2) & (f1 > s1)) | ((f2 >= s2) & (f1 < s1))
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out = np.zeros(n, dtype=np.int32)
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for i in range(lookback, n):
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out[i] = int(np.sum(cross[i - lookback + 1 : i + 1]))
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return out
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def _rolling_max(arr: np.ndarray, window: int) -> np.ndarray:
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s = pd.Series(arr)
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return s.shift(1).rolling(window, min_periods=1).max().to_numpy()
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def _rolling_min(arr: np.ndarray, window: int) -> np.ndarray:
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s = pd.Series(arr)
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return s.shift(1).rolling(window, min_periods=1).min().to_numpy()
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def build_v4_signals(md: V4Market, p: V4Params, 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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rb = max(p.adx_rising_bars, 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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f2 = np.roll(md.fast, 2)
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s2 = np.roll(md.slow, 2)
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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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htf1 = np.roll(md.htf, 1)
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adx1 = np.roll(md.adx, 1)
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adx_rb = np.roll(md.adx, 1 + rb)
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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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slow_old = np.roll(md.slow, lb)
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atr_pips = atr1 / pip
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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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adx_rising = adx1 > adx_rb
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sess = _session_ok(np.roll(md.hours, 1), p.session_start, p.session_end)
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chop = _rolling_cross_count(md.fast, md.slow, p.chop_lookback)
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chop_ok = chop <= p.max_chop_crosses
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gap_ok = np.abs(f1 - s1) >= atr1 * p.min_ema_gap_atr
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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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slow_up = s1 > slow_old
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slow_dn = s1 < slow_old
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long_regime = (f1 > s1) & (c1 > htf1) & (c1 > s1) & slow_up
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short_regime = (f1 < s1) & (c1 < htf1) & (c1 < s1) & slow_dn
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regime = long_regime | short_regime
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base = regime & gap_ok & atr_ok & adx_ok & adx_rising & sess & chop_ok
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bull_cross = (f2 <= s2) & (f1 > s1)
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bear_cross = (f2 >= s2) & (f1 < s1)
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swing_hi = _rolling_max(md.high, p.pullback_swing_bars)
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swing_lo = _rolling_min(md.low, p.pullback_swing_bars)
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depth_long = (swing_hi - l1) >= atr1 * p.pullback_min_depth_atr
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depth_short = (h1 - swing_lo) >= atr1 * p.pullback_min_depth_atr
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pb_long = long_regime & (l1 <= f1) & (c1 > f1) & depth_long & bull_bar
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pb_short = short_regime & (h1 >= f1) & (c1 < f1) & depth_short & bear_bar
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if p.entry_mode == 0:
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buy_raw, sell_raw = bull_cross, bear_cross
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elif p.entry_mode == 2:
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buy_raw, sell_raw = pb_long, pb_short
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else:
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buy_raw = bull_cross | pb_long
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sell_raw = bear_cross | pb_short
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buy = buy_raw & base & di_long
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sell = sell_raw & base & di_short
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warm = max(p.slow_ema + lb + p.chop_lookback + 5, 40)
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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_v4(md: V4Market, symbol: str, p: V4Params, costs, pip: float, point: float) -> V4Result:
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buy_sig, sell_sig = build_v4_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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tp1_px = 0.0
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lot_frac = 1.0
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tp1_done = False
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trail = 0.0
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last_entry_i = -10_000
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def calc_profit(entry_px: float, exit_px: float, s: str, frac: float = 1.0) -> float:
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lot = p.lot_size * frac
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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, lot, entry_px, exit_px)
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comm = costs.commission_per_lot * lot * 2.0
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return float(pr) - comm if pr is not None else -comm
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warm = max(p.slow_ema + p.chop_lookback + 10, 40)
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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, lot_frac)
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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_partial_tp and not tp1_done and high[i] >= tp1_px:
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pr1 = calc_profit(entry, tp1_px - half, side, p.tp1_close_pct)
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balance += pr1
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tp1_done = True
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lot_frac = 1.0 - p.tp1_close_pct
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sl_px = max(sl_px, entry + be_off)
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tp_px = entry + atr1 * p.tp2_atr_mult if atr1 > 0 else tp_px
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if tp1_done and p.use_trail_after_tp1 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 tp1_done else "sl")
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pr = calc_profit(entry, eff_sl - half, side, lot_frac)
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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, lot_frac)
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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": "tp2" if tp1_done else "tp"})
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closed = True
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elif not closed and side == "SELL":
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if p.use_partial_tp and not tp1_done and low[i] <= tp1_px:
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pr1 = calc_profit(entry, tp1_px + half, side, p.tp1_close_pct)
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balance += pr1
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tp1_done = True
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lot_frac = 1.0 - p.tp1_close_pct
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sl_px = min(sl_px, entry - be_off)
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tp_px = entry - atr1 * p.tp2_atr_mult if atr1 > 0 else tp_px
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if tp1_done and p.use_trail_after_tp1 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 tp1_done else "sl")
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pr = calc_profit(entry, eff_sl + half, side, lot_frac)
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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, lot_frac)
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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": "tp2" if tp1_done else "tp"})
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closed = True
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if closed:
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side = None
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tp1_done = False
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lot_frac = 1.0
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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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tp1_px = entry + atr1 * p.tp1_atr_mult
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tp_px = entry + atr1 * (p.tp2_atr_mult if p.use_partial_tp else p.tp1_atr_mult)
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tp1_done = False
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lot_frac = 1.0
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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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tp1_px = entry - atr1 * p.tp1_atr_mult
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tp_px = entry - atr1 * (p.tp2_atr_mult if p.use_partial_tp else p.tp1_atr_mult)
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tp1_done = False
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lot_frac = 1.0
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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, lot_frac) + costs.commission_per_lot * p.lot_size * lot_frac * 2.0
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elif side == "SELL":
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mark += calc_profit(entry, float(close[i - 1]), side, lot_frac) + costs.commission_per_lot * p.lot_size * lot_frac * 2.0
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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, lot_frac)
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balance += pr
|
||||
trades.append({"side": side, "open_i": entry_i, "close_i": len(md.df) - 1, "profit": pr, "exit_reason": "eod"})
|
||||
|
||||
eq = pd.Series(equity[: len(md.df)], index=md.df.index[: len(equity)])
|
||||
net = balance - p.initial_balance
|
||||
wins = [t["profit"] for t in trades if t["profit"] > 0]
|
||||
losses = [t["profit"] for t in trades if t["profit"] <= 0]
|
||||
gp = sum(wins) if wins else 0.0
|
||||
gl = abs(sum(losses)) if losses else 0.0
|
||||
pf = gp / gl if gl > 0 else 0.0
|
||||
wr = 100.0 * len(wins) / len(trades) if trades else 0.0
|
||||
dd = abs(float(((eq - eq.cummax()) / eq.cummax() * 100).min())) if len(eq) else 0.0
|
||||
rets = eq.pct_change().dropna()
|
||||
sharpe = float(rets.mean() / rets.std() * np.sqrt(252 * 24 * 4)) if len(rets) > 1 and rets.std() > 0 else 0.0
|
||||
return V4Result(net, len(trades), wr, pf, dd, sharpe, trades)
|
||||
|
||||
|
||||
def sample_v4(rng) -> V4Params:
|
||||
return V4Params(
|
||||
fast_ema=rng.randint(7, 13),
|
||||
slow_ema=rng.choice([p for p in range(30, 53, 2)]),
|
||||
entry_mode=rng.choice([0, 1, 1, 1]),
|
||||
min_ema_gap_atr=round(rng.uniform(0.08, 0.25), 2),
|
||||
chop_lookback=rng.choice([16, 20, 24, 32]),
|
||||
max_chop_crosses=rng.choice([0, 1, 1, 2]),
|
||||
pullback_swing_bars=rng.choice([8, 12, 16]),
|
||||
pullback_min_depth_atr=round(rng.uniform(0.2, 0.6), 2),
|
||||
adx_rising_bars=rng.choice([2, 3, 4, 5]),
|
||||
cooldown_bars=rng.choice([3, 4, 5, 6, 8]),
|
||||
atr_period=rng.choice([10, 14, 20]),
|
||||
atr_sl_mult=round(rng.uniform(1.8, 2.8), 2),
|
||||
tp1_atr_mult=round(rng.uniform(1.4, 2.2), 2),
|
||||
tp1_close_pct=rng.choice([0.4, 0.5, 0.5, 0.6]),
|
||||
tp2_atr_mult=round(rng.uniform(4.5, 7.0), 2),
|
||||
max_bars_in_trade=rng.choice([64, 80, 96]),
|
||||
htf_ema_period=rng.choice([50, 100, 200]),
|
||||
adx_min=round(rng.uniform(18, 26), 1),
|
||||
adx_max=round(rng.uniform(38, 50), 1),
|
||||
min_atr_pips=round(rng.uniform(2.0, 5.0), 1),
|
||||
max_atr_pips=round(rng.uniform(18, 28), 1),
|
||||
slope_lookback=rng.choice([4, 5, 6]),
|
||||
require_bullish_bar=rng.choice([True, True, False]),
|
||||
use_di_filter=rng.choice([True, True, False]),
|
||||
use_partial_tp=rng.choice([True, True, False]),
|
||||
use_trail_after_tp1=rng.choice([True, True, False]),
|
||||
trail_atr_mult=round(rng.uniform(1.1, 1.8), 2),
|
||||
session_start=rng.choice([7, 8]),
|
||||
session_end=rng.choice([20, 21, 22]),
|
||||
max_spread_pips=rng.choice([6, 8]),
|
||||
)
|
||||
|
||||
|
||||
def write_v4_set(p: V4Params, path) -> None:
|
||||
lines = [
|
||||
"; SimpleEMA v4 — regime dual entry + partial TP",
|
||||
"Timeframe=16388",
|
||||
f"FastEmaPeriod={p.fast_ema}",
|
||||
f"SlowEmaPeriod={p.slow_ema}",
|
||||
f"EntryMode={p.entry_mode}",
|
||||
f"MinEmaGapAtr={p.min_ema_gap_atr}",
|
||||
f"ChopLookback={p.chop_lookback}",
|
||||
f"MaxChopCrosses={p.max_chop_crosses}",
|
||||
f"PullbackSwingBars={p.pullback_swing_bars}",
|
||||
f"PullbackMinDepthAtr={p.pullback_min_depth_atr}",
|
||||
f"AdxRisingBars={p.adx_rising_bars}",
|
||||
f"CooldownBars={p.cooldown_bars}",
|
||||
f"AtrPeriod={p.atr_period}",
|
||||
f"AtrSlMult={p.atr_sl_mult}",
|
||||
f"Tp1AtrMult={p.tp1_atr_mult}",
|
||||
f"Tp1ClosePct={p.tp1_close_pct}",
|
||||
f"Tp2AtrMult={p.tp2_atr_mult}",
|
||||
f"MaxBarsInTrade={p.max_bars_in_trade}",
|
||||
f"HtfEmaPeriod={p.htf_ema_period}",
|
||||
f"AdxPeriod={p.adx_period}",
|
||||
f"AdxMin={p.adx_min}",
|
||||
f"AdxMax={p.adx_max}",
|
||||
f"MinAtrPips={p.min_atr_pips}",
|
||||
f"MaxAtrPips={p.max_atr_pips}",
|
||||
f"SlopeLookback={p.slope_lookback}",
|
||||
f"RequireBullishBar={'true' if p.require_bullish_bar else 'false'}",
|
||||
f"UseDiFilter={'true' if p.use_di_filter else 'false'}",
|
||||
f"UsePartialTp={'true' if p.use_partial_tp else 'false'}",
|
||||
f"UseTrailAfterTp1={'true' if p.use_trail_after_tp1 else 'false'}",
|
||||
f"TrailAtrMult={p.trail_atr_mult}",
|
||||
f"BeOffsetPips={p.be_offset_pips}",
|
||||
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")
|
||||
Reference in New Issue
Block a user