0.93
This commit is contained in:
qq564020069
@@ -139,6 +139,11 @@ class Config:
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COST_RATE = 0.0001 # 单边点差+佣金（forex/metals）
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MAX_OPEN_POSITIONS = 4 # 最多同时持仓品种数
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MAX_LOT_PER_TRADE = 1.0 # 收益优先上限；仍受 MT5 品种规格和保证金约束
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# 手数校准：
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# - 黄金（XAUUSD）波动金额显著更大，固定小手数避免整体风险失控
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# - 其他品种在 ATR 风控手数基础上乘一个系数放大收益弹性
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XAUUSD_FIXED_LOT = 0.10
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OTHER_LOT_MULTIPLIER = 2.0
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MIN_TRADE_EXPOSURE = 0.05 # |tanh(factor)| 小于该值时视为空仓，回测/实盘共用
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# ── 策略参数 ──────────────────────────────────────────
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+13
-24
@@ -2,23 +2,15 @@
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live_trade.py — 自动交易启动脚本
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使用方法：
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python live_trade.py # forex 双因子模式（forex_v1 + forex_v2，默认）
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python live_trade.py # XAGUSD + US500 + US100 三品种（默认）
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python live_trade.py --dry-run # 模拟运行（不下单，只打印信号）
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python live_trade.py --symbols EURUSD # 只交易指定品种
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python live_trade.py --symbols XAGUSD # 只交易指定品种
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python live_trade.py --single # 使用 best_mt5_strategy.json 单公式（回退）
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已启用的有效策略（forex 双因子）：
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forex_v1：strategies/archive/best_forex_20250705_pre_refactor.json
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公式：AROON_OSC_25 → TS_MIN_10 → EMA_20 → CS_SCALE → SIGN → CS_SCALE → EMA_5 → MOMENTUM_5
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回测：8年，Sharpe 0.879，MDD 7.03%，3折WF全正，2x成本仍盈利
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forex_v2：strategies/best_forex.json
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公式：DMI_DIFF_14 → HL_RANGE → TS_MIN_20 → SUB → TS_MIN_20 → TS_MEAN_20 → CS_SCALE → WMA
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回测：8年，Sharpe 0.684，MDD 6.88%，2x成本仍盈利
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信号合并规则：
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两条因子信号取算术平均（tanh 压缩后）。
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若两条信号方向相反 → 均值趋近 0 → 不开仓（等待两者达成一致）。
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若两条信号方向一致 → 均值绝对值更大 → 较大仓位比例。
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当前启用策略（收益优先）：
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XAGUSD：strategies/best_XAGUSD.json（precious_metals_v1）
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US500.cash：strategies/best_US500.cash.json（us500_v1）
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US100.cash：strategies/best_US100.cash.json（us100_v1）
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注意：
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- 需要 MT5 终端已登录并允许自动交易
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@@ -37,14 +29,11 @@ from config import Config
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from strategy_manager.runner import MT5StrategyRunner
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from loguru import logger
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# ── 默认交易品种：forex 组 + metals_comm 组 ──────────────────────────────────
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# forex 组：forex_v1 + forex_v2（8年数据，充分验证）
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# metals_comm 组：metals_comm_v2（1.37年数据，统计显著性有限，MDD 13.62%）
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_DEFAULT_SYMBOLS = ["EURUSD", "USDJPY", "XAUUSD", "AAVUSD", "COCOA.c"]
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# 收益优先：白银 + 标普500 + 纳指100，各用单品种最优因子
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_DEFAULT_SYMBOLS = ["XAGUSD", "US500.cash", "US100.cash"]
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def main():
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# 命令行参数处理
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dry_run = "--dry-run" in sys.argv
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single = "--single" in sys.argv
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sym_override = None
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@@ -52,7 +41,6 @@ def main():
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idx = sys.argv.index("--symbols")
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sym_override = sys.argv[idx+1:]
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# 默认限定有效策略品种
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if sym_override:
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Config.SYMBOLS = sym_override
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logger.info(f"[live_trade] 品种覆盖: {Config.SYMBOLS}")
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@@ -67,14 +55,15 @@ def main():
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logger.info("[live_trade] 单公式模式：所有品种共用 best_mt5_strategy.json")
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logger.info("=" * 60)
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logger.info(" AlphaGPT 自动交易启动 [多品种多因子模式]")
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logger.info(" AlphaGPT 自动交易启动 [XAGUSD + US500 + US100]")
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logger.info(f" 品种: {Config.SYMBOLS}")
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logger.info(f" 周期: H1")
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logger.info(f" forex 策略: forex_v1 (archive) + forex_v2 (best_forex)")
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logger.info(f" metals 策略: metals_comm_v2 (best_metals_comm) ⚠️ 仅1.37年数据")
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logger.info(f" 信号合并: 各品种多公式均值（反向抵消，同向叠加）")
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logger.info(f" XAGUSD: best_XAGUSD.json (precious_metals_v1)")
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logger.info(f" US500.cash: best_US500.cash.json (us500_v1)")
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logger.info(f" US100.cash: best_US100.cash.json (us100_v1)")
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logger.info(f" 信号模式: {Config.SIGNAL_MODE}")
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logger.info(f" 出场模式: {Config.EXIT_MODE}")
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logger.info(f" 单笔风险: {Config.RISK_PER_TRADE * 100:.1f}% ATR")
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logger.info("=" * 60)
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runner = MT5StrategyRunner()
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@@ -0,0 +1,395 @@
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"""
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rigorous_backtest_audit.py — 对仓库内所有可用策略做独立严谨回测
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不复用训练 reward；从第一性原理计算 PnL。
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支持：全样本 / 前后半段 / Walk-Forward 4折 / 成本压力 / 单品种拆解。
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"""
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from __future__ import annotations
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import json
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import math
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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 torch
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sys.path.insert(0, str(Path(__file__).parent))
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from config import Config
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from data_pipeline.data_manager import MT5DataManager
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from data_pipeline.fetcher import MT5DataFetcher
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from model_core.vocab import FORMULA_VOCAB, VOCAB_VERSION
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from model_core.vm import StackVM
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from strategy_manager.signal import compute_target_positions_stateless
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PERIODS_PER_YEAR = 6240
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MIN_EXPOSURE = getattr(Config, "MIN_TRADE_EXPOSURE", 0.05)
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# 品种组成本（单边 log）
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COST_BY_GROUP = {
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"forex": 0.00015,
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"precious_metals": 0.00020,
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"index": 0.00030,
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}
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# 待测策略：name -> (path, group, symbols_override or None)
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CANDIDATES: dict[str, dict] = {
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"precious_metals_current": {
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"path": "strategies/best_precious_metals.json",
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"group": "precious_metals",
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"symbols": None,
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},
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"precious_metals_v1": {
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"path": "strategies/precious_metals_v1.json",
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"group": "precious_metals",
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"symbols": None,
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"formula_key": "formula_tokens",
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},
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"index_current": {
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"path": "strategies/best_index.json",
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"group": "index",
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"symbols": None,
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},
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}
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def decode_formula(tokens: list[int]) -> str:
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names = FORMULA_VOCAB.token_names
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return " -> ".join(names[t] if 0 <= t < len(names) else f"?{t}" for t in tokens)
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def load_candidate(path: str, formula_key: str = "formula") -> dict | None:
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p = Path(path)
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if not p.exists():
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return None
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data = json.load(p.open(encoding="utf-8"))
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ver = data.get("vocab_version")
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if ver and ver != VOCAB_VERSION:
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return {"error": f"vocab mismatch {ver} != {VOCAB_VERSION}"}
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formula = data.get(formula_key) or data.get("formula")
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if not formula:
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return {"error": "no formula"}
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return {
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"formula": [int(t) for t in formula],
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"best_score": data.get("best_score") or data.get("train_best_score"),
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"status": data.get("status", ""),
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"readable": data.get("formula_readable") or decode_formula(formula),
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}
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def independent_backtest(
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factor: torch.Tensor,
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target_ret: torch.Tensor,
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cost_rate: float,
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) -> dict:
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pos = compute_target_positions_stateless(factor)
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prev = torch.zeros_like(pos)
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prev[:, 1:] = pos[:, :-1]
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turnover = (pos - prev).abs()
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pnl = pos * target_ret - turnover * cost_rate
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port = pnl.mean(dim=0)
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T = int(port.shape[0])
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def _ann(x: torch.Tensor) -> float:
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return float(x.mean().item() * PERIODS_PER_YEAR)
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def _sharpe(x: torch.Tensor) -> float:
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m, s = x.mean().item(), x.std(unbiased=False).item()
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return float(m / (s + 1e-8) * math.sqrt(PERIODS_PER_YEAR)) if s > 1e-8 else 0.0
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def _sortino(x: torch.Tensor) -> float:
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m = x.mean().item()
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down = x[x < 0]
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ds = down.std(unbiased=False).item() if down.numel() else 0.0
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floor = max(x.std(unbiased=False).item() * 0.2, 1e-8)
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ds = max(ds, floor)
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return float(np.clip(m / ds * math.sqrt(PERIODS_PER_YEAR), -20, 20))
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def _mdd(x: torch.Tensor) -> float:
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cum = torch.cumsum(x, dim=0)
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peak = torch.cummax(cum, dim=0).values
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return float((peak - cum).max().item())
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half = T // 2
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h1, h2 = port[:half], port[half:]
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# Walk-forward 4 folds (val only)
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n_folds = 4
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fold_size = max(1, T // n_folds)
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wf = []
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for k in range(1, n_folds):
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vs = fold_size * k
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ve = min(vs + fold_size, T)
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if vs >= T:
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break
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vp = port[vs:ve]
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wf.append({
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"fold": k,
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"bars": ve - vs,
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"ann_ret": _ann(vp),
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"sharpe": _sharpe(vp),
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"mdd": _mdd(vp),
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"positive": _ann(vp) > 0,
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})
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cost_stress = {}
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for mult in (1.0, 2.0, 3.0, 5.0):
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sp = (pos * target_ret - turnover * cost_rate * mult).mean(dim=0)
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cost_stress[f"{mult}x"] = {
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"ann_ret": _ann(sp),
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"sharpe": _sharpe(sp),
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"profitable": _ann(sp) > 0,
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}
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long_r = (pos > MIN_EXPOSURE).float().mean().item()
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short_r = (pos < -MIN_EXPOSURE).float().mean().item()
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max_side = max(long_r, short_r)
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per_sym = []
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N = pnl.shape[0]
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for n in range(N):
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sp = pnl[n]
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per_sym.append({
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"total_ret": float(sp.sum().item()),
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"ann_ret": _ann(sp),
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"sharpe": _sharpe(sp),
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"sortino": _sortino(sp),
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"mdd": _mdd(sp),
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"long_pct": float((pos[n] > MIN_EXPOSURE).float().mean().item()),
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"short_pct": float((pos[n] < -MIN_EXPOSURE).float().mean().item()),
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})
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ann = _ann(port)
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sharpe = _sharpe(port)
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mdd = _mdd(port)
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verdict = "VALID"
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issues: list[str] = []
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if ann < 0.02:
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verdict = "INVALID"
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issues.append(f"年化 {ann*100:.2f}% < 2%")
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if sharpe < 0.5:
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if verdict == "VALID":
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verdict = "SUSPICIOUS"
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issues.append(f"Sharpe {sharpe:.3f} < 0.5")
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if mdd > 0.10:
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if verdict == "VALID":
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verdict = "SUSPICIOUS"
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issues.append(f"MDD {mdd*100:.2f}% > 10%")
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if mdd > 0.20:
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verdict = "INVALID"
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if max_side > 0.85:
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if verdict == "VALID":
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verdict = "SUSPICIOUS"
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issues.append(f"单边占比 {max_side*100:.1f}% > 85%")
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if _ann(h1) * _ann(h2) < 0:
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if verdict == "VALID":
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verdict = "SUSPICIOUS"
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issues.append(f"前后半段年化异号 H1={_ann(h1)*100:.2f}% H2={_ann(h2)*100:.2f}%")
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wf_pos = sum(1 for w in wf if w["positive"])
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if wf and wf_pos < len(wf) * 0.5:
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if verdict == "VALID":
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verdict = "SUSPICIOUS"
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issues.append(f"WF 仅 {wf_pos}/{len(wf)} 折为正")
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if not cost_stress["2.0x"]["profitable"]:
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if verdict == "VALID":
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verdict = "SUSPICIOUS"
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issues.append("2x 成本下亏损")
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return {
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"verdict": verdict,
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"issues": issues,
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"T": T,
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"years": T / PERIODS_PER_YEAR,
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"ann_ret": ann,
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"total_ret": float(port.sum().item()),
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"sharpe": sharpe,
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"sortino": _sortino(port),
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"mdd": mdd,
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"calmar": ann / (mdd + 1e-8),
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"long_pct": long_r,
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"short_pct": short_r,
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"max_side": max_side,
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"h1_ann": _ann(h1),
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"h2_ann": _ann(h2),
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"h1_sharpe": _sharpe(h1),
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"h2_sharpe": _sharpe(h2),
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"walk_forward": wf,
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"cost_stress": cost_stress,
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"per_symbol": per_sym,
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"n_pos_syms": sum(1 for p in per_sym if p["total_ret"] > 0),
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"n_syms": N,
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}
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def run_one(name: str, meta: dict, fetcher: MT5DataFetcher) -> dict | None:
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loaded = load_candidate(meta["path"], meta.get("formula_key", "formula"))
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if loaded is None:
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print(f"\n[{name}] SKIP: file missing {meta['path']}")
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return None
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if "error" in loaded:
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print(f"\n[{name}] SKIP: {loaded['error']}")
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return None
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group = meta["group"]
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symbols = meta.get("symbols") or Config.SYMBOL_GROUPS[group]
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cost = COST_BY_GROUP.get(group, Config.COST_RATE)
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print(f"\n{'='*72}")
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print(f" [{name}] group={group}")
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print(f" symbols={symbols}")
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print(f" formula: {loaded['readable']}")
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print(f" train_score={loaded.get('best_score')} status={loaded.get('status') or '-'}")
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print(f" cost_rate={cost}")
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print(f"{'='*72}")
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orig = Config.SYMBOLS[:]
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Config.SYMBOLS = list(symbols)
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try:
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mgr = MT5DataManager(fetcher)
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mgr.load()
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syms = mgr.symbols
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T = mgr.raw_dict["open"].shape[1]
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times = mgr.raw_dict.get("time")
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t0 = int(times[0, 0]) if times is not None else 0
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t1 = int(times[0, -1]) if times is not None else 0
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from datetime import datetime, timezone
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if t0 and t1:
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d0 = datetime.fromtimestamp(t0, tz=timezone.utc).strftime("%Y-%m-%d")
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d1 = datetime.fromtimestamp(t1, tz=timezone.utc).strftime("%Y-%m-%d")
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print(f" loaded: {syms} T={T} ({T/PERIODS_PER_YEAR:.2f}y) {d0} .. {d1}")
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else:
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print(f" loaded: {syms} T={T} ({T/PERIODS_PER_YEAR:.2f}y)")
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vm = StackVM()
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factor = vm.execute(loaded["formula"], mgr.feat_tensor)
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if factor is None:
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print(" ERROR: formula execution failed")
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return {"error": "vm failed"}
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bt = independent_backtest(factor, mgr.target_ret, cost)
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bt["name"] = name
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bt["group"] = group
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bt["symbols"] = syms
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bt["formula"] = loaded["formula"]
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bt["readable"] = loaded["readable"]
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bt["train_score"] = loaded.get("best_score")
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print(f"\n 组合: ann={bt['ann_ret']*100:+.2f}% Sharpe={bt['sharpe']:+.3f} "
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f"Sortino={bt['sortino']:+.3f} MDD={bt['mdd']*100:.2f}% "
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f"Calmar={bt['calmar']:+.2f}")
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print(f" 多空: L={bt['long_pct']*100:.1f}% S={bt['short_pct']*100:.1f}% "
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f"max_side={bt['max_side']*100:.1f}%")
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print(f" 一致性: H1={bt['h1_ann']*100:+.2f}% H2={bt['h2_ann']*100:+.2f}% "
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f"H1_S={bt['h1_sharpe']:+.2f} H2_S={bt['h2_sharpe']:+.2f}")
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wf_pos = sum(1 for w in bt["walk_forward"] if w["positive"])
|
||||
print(f" WF: {wf_pos}/{len(bt['walk_forward'])} 折正收益")
|
||||
for mult, cs in bt["cost_stress"].items():
|
||||
print(f" cost {mult}: ann={cs['ann_ret']*100:+.2f}% sharpe={cs['sharpe']:+.3f} "
|
||||
f"ok={'Y' if cs['profitable'] else 'N'}")
|
||||
print(f"\n 品种级:")
|
||||
for i, sym in enumerate(syms):
|
||||
ps = bt["per_symbol"][i]
|
||||
tag = "OK" if ps["total_ret"] > 0 else "X"
|
||||
print(f" {sym:14s}: ann={ps['ann_ret']*100:+7.2f}% Sharpe={ps['sharpe']:+6.3f} "
|
||||
f"MDD={ps['mdd']*100:5.2f}% L/S={ps['long_pct']*100:.0f}/{ps['short_pct']*100:.0f} [{tag}]")
|
||||
print(f"\n 判定: {bt['verdict']}")
|
||||
for iss in bt["issues"]:
|
||||
print(f" ! {iss}")
|
||||
if not bt["issues"]:
|
||||
print(" 全部检查通过")
|
||||
|
||||
# 单品种拆解（同一公式，N=1）
|
||||
print(f"\n --- 单品种独立回测（同公式，逐品种加载）---")
|
||||
solo_results = []
|
||||
for sym in symbols:
|
||||
Config.SYMBOLS = [sym]
|
||||
try:
|
||||
sm = MT5DataManager(fetcher)
|
||||
sm.load()
|
||||
if sym not in sm.symbols:
|
||||
continue
|
||||
f1 = vm.execute(loaded["formula"], sm.feat_tensor)
|
||||
if f1 is None:
|
||||
continue
|
||||
sb = independent_backtest(f1, sm.target_ret, cost)
|
||||
solo_results.append({"symbol": sym, **sb})
|
||||
print(f" {sym:14s} solo: ann={sb['ann_ret']*100:+7.2f}% "
|
||||
f"Sharpe={sb['sharpe']:+.3f} MDD={sb['mdd']*100:.2f}% "
|
||||
f"verdict={sb['verdict']}")
|
||||
except Exception as e:
|
||||
print(f" {sym:14s} solo ERROR: {e}")
|
||||
bt["solo"] = solo_results
|
||||
return bt
|
||||
finally:
|
||||
Config.SYMBOLS = orig
|
||||
|
||||
|
||||
def main():
|
||||
offline = "--offline" in sys.argv
|
||||
mode = "offline" if offline else "MT5/live"
|
||||
print(f"\nRigorous Backtest Audit | vocab={VOCAB_VERSION} | mode={mode}\n")
|
||||
|
||||
results: dict[str, dict] = {}
|
||||
with MT5DataFetcher(offline=offline) as fetcher:
|
||||
if not offline:
|
||||
try:
|
||||
fetcher.connect()
|
||||
except Exception as e:
|
||||
print(f"MT5 connect failed: {e}\nRetry with --offline if cache exists.")
|
||||
sys.exit(1)
|
||||
for name, meta in CANDIDATES.items():
|
||||
r = run_one(name, meta, fetcher)
|
||||
if r:
|
||||
results[name] = r
|
||||
|
||||
print(f"\n\n{'='*88}")
|
||||
print(" 汇总（组合等权）")
|
||||
print(f"{'='*88}")
|
||||
print(f"{'策略':<26} {'年化%':>8} {'Sharpe':>8} {'MDD%':>8} {'WF+':>6} {'2x成本':>8} {'盈利品种':>10} {'判定':>12}")
|
||||
print("-" * 88)
|
||||
for name, r in results.items():
|
||||
if "error" in r:
|
||||
print(f"{name:<26} ERROR")
|
||||
continue
|
||||
wf_pos = sum(1 for w in r["walk_forward"] if w["positive"])
|
||||
wf_n = len(r["walk_forward"])
|
||||
ok2x = "Y" if r["cost_stress"]["2.0x"]["profitable"] else "N"
|
||||
print(f"{name:<26} {r['ann_ret']*100:>8.2f} {r['sharpe']:>8.3f} {r['mdd']*100:>8.2f} "
|
||||
f"{wf_pos}/{wf_n:<4} {ok2x:>8} {r['n_pos_syms']}/{r['n_syms']:<8} {r['verdict']:>12}")
|
||||
|
||||
# 单品种最优
|
||||
print(f"\n{'='*88}")
|
||||
print(" 单品种拆解（同公式 solo）")
|
||||
print(f"{'='*88}")
|
||||
solo_rows = []
|
||||
for name, r in results.items():
|
||||
for s in r.get("solo", []):
|
||||
solo_rows.append((s["ann_ret"], name, s["symbol"], s))
|
||||
solo_rows.sort(reverse=True, key=lambda x: x[0])
|
||||
print(f"{'品种':<14} {'来源策略':<26} {'年化%':>8} {'Sharpe':>8} {'MDD%':>8} {'判定':>12}")
|
||||
print("-" * 88)
|
||||
for _, name, sym, s in solo_rows:
|
||||
print(f"{sym:<14} {name:<26} {s['ann_ret']*100:>8.2f} {s['sharpe']:>8.3f} "
|
||||
f"{s['mdd']*100:>8.2f} {s['verdict']:>12}")
|
||||
|
||||
out = Path("backtest_output/rigorous_audit.json")
|
||||
out.parent.mkdir(parents=True, exist_ok=True)
|
||||
serial = {}
|
||||
for k, v in results.items():
|
||||
if "error" in v:
|
||||
serial[k] = v
|
||||
continue
|
||||
serial[k] = {key: val for key, val in v.items() if key != "solo" or True}
|
||||
if "solo" in v:
|
||||
serial[k]["solo"] = [
|
||||
{kk: vv for kk, vv in s.items() if kk not in ("walk_forward", "cost_stress", "per_symbol", "issues")}
|
||||
for s in v["solo"]
|
||||
]
|
||||
out.write_text(json.dumps(serial, indent=2, ensure_ascii=False), encoding="utf-8")
|
||||
print(f"\n详细 JSON → {out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -179,6 +179,23 @@ class MT5RiskEngine:
|
||||
# 含义：1个ATR波动时的盈亏 = target_risk_usd
|
||||
desired_lot = target_risk_usd / (atr_price * value_per_unit)
|
||||
|
||||
# ── 手数校准：黄金固定，其它品种按系数放大 ─────────────────
|
||||
# 需求：保持黄金始终只下 0.1 手，其它品种按某个系数加大手数。
|
||||
try:
|
||||
xau_fixed = float(getattr(Config, "XAUUSD_FIXED_LOT", 0.10))
|
||||
except Exception:
|
||||
xau_fixed = 0.10
|
||||
try:
|
||||
other_mult = float(getattr(Config, "OTHER_LOT_MULTIPLIER", 1.0))
|
||||
except Exception:
|
||||
other_mult = 1.0
|
||||
|
||||
sym_norm = symbol.upper()
|
||||
if sym_norm.startswith("XAUUSD"):
|
||||
desired_lot = xau_fixed
|
||||
else:
|
||||
desired_lot = desired_lot * other_mult
|
||||
|
||||
# 舍入到 volume_step，clamp
|
||||
step = symbol_info.volume_step
|
||||
lot = round(desired_lot / step) * step
|
||||
|
||||
@@ -86,17 +86,20 @@ class MT5StrategyRunner:
|
||||
try:
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
if not isinstance(data, dict) or "formula" not in data:
|
||||
if not isinstance(data, dict):
|
||||
return None
|
||||
formula = data.get("formula") or data.get("formula_tokens")
|
||||
if not formula:
|
||||
return None
|
||||
ver = data.get("vocab_version", "unknown")
|
||||
if ver != _CURRENT_VER:
|
||||
logger.warning(f"[Runner] {path.name}: vocab_version={ver} != {_CURRENT_VER}, skip")
|
||||
return None
|
||||
score = data.get("best_score", 0.0)
|
||||
score = data.get("best_score") or data.get("train_best_score") or 0.0
|
||||
if score <= 0.0:
|
||||
logger.warning(f"[Runner] {path.name}: best_score={score:.4f} <= 0, skip (invalid strategy)")
|
||||
return None
|
||||
return [int(t) for t in data["formula"]]
|
||||
return [int(t) for t in formula]
|
||||
except Exception as exc:
|
||||
logger.warning(f"[Runner] {path.name}: 加载失败 {exc}")
|
||||
return None
|
||||
@@ -141,7 +144,7 @@ class MT5StrategyRunner:
|
||||
_add(_load_formula(archive_dir / "best_forex_20250705_pre_refactor.json"),
|
||||
"archive_forex_v1")
|
||||
|
||||
# 4. metals_comm 组共享策略（metals_comm_v2）
|
||||
# 4. metals_comm 组共享策略（metals_comm_v2，旧版）
|
||||
if sym in metals_comm_group:
|
||||
_add(_load_formula(strategies_dir / "best_metals_comm.json"),
|
||||
"best_metals_comm(v2)")
|
||||
|
||||
Reference in New Issue
Block a user