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>
341 lines
10 KiB
Python
341 lines
10 KiB
Python
"""
|
|
USDJPYBuster — bar backtest mirroring main.mq5 inputs.
|
|
|
|
Outputs in this folder:
|
|
backtest_report.json, trades.csv, report.png,
|
|
equity_curve.png, drawdown.png, monthly_returns.png,
|
|
pnl_distribution.png, exit_reasons.png
|
|
|
|
Usage:
|
|
python run_backtest.py
|
|
python run_backtest.py --start 2021-01-01 --end 2026-01-01
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import argparse
|
|
import json
|
|
import sys
|
|
from dataclasses import asdict, dataclass
|
|
from datetime import datetime
|
|
from pathlib import Path
|
|
|
|
import matplotlib
|
|
matplotlib.use("Agg")
|
|
import matplotlib.pyplot as plt
|
|
import MetaTrader5 as mt5
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
ROOT = Path(__file__).resolve().parents[3]
|
|
sys.path.insert(0, str(ROOT / "backtesting" / "MT5"))
|
|
|
|
from cluster_audit.backtest_core import ( # noqa: E402
|
|
BacktestReport,
|
|
CostModel,
|
|
Trade,
|
|
build_report,
|
|
calc_profit,
|
|
fill_price,
|
|
load_bars,
|
|
resolve_symbol,
|
|
)
|
|
from indicator_utils import calculate_adx, calculate_atr, calculate_dmi, calculate_ema, calculate_rsi # noqa: E402
|
|
|
|
STRATEGY_ID = "USDJPYBuster"
|
|
|
|
|
|
@dataclass
|
|
class SimState:
|
|
side: str | None = None
|
|
entry: float = 0.0
|
|
entry_i: int = 0
|
|
entry_time: object = None
|
|
sl: float = 0.0
|
|
tp: float = 0.0
|
|
bars_against: int = 0
|
|
rsi_against: bool = False
|
|
|
|
|
|
def run_single_position(
|
|
df: pd.DataFrame,
|
|
symbol: str,
|
|
point: float,
|
|
costs: CostModel,
|
|
lot: float,
|
|
tf_label: str,
|
|
period_label: str,
|
|
params: dict,
|
|
initial_balance: float,
|
|
on_bar,
|
|
) -> BacktestReport:
|
|
trades: list[Trade] = []
|
|
equity = [initial_balance]
|
|
st = SimState()
|
|
|
|
def close(i: int, mid: float, reason: str) -> None:
|
|
nonlocal st
|
|
if st.side is None:
|
|
return
|
|
exit_px = fill_price(mid, point, costs, st.side, entry=False)
|
|
commission = costs.commission_per_lot * lot * 2.0
|
|
profit = calc_profit(symbol, st.side, lot, st.entry, exit_px) - commission
|
|
trades.append(
|
|
Trade(
|
|
side=st.side,
|
|
open_time=st.entry_time,
|
|
close_time=df.index[i],
|
|
open_price=st.entry,
|
|
close_price=exit_px,
|
|
volume=lot,
|
|
profit=profit,
|
|
bars_held=i - st.entry_i,
|
|
exit_reason=reason,
|
|
)
|
|
)
|
|
equity.append(equity[-1] + profit)
|
|
st = SimState()
|
|
|
|
def open_pos(i: int, side: str, mid: float) -> None:
|
|
nonlocal st
|
|
st.side = side
|
|
st.entry = fill_price(mid, point, costs, side, entry=True)
|
|
st.entry_i = i
|
|
st.entry_time = df.index[i]
|
|
|
|
for i in range(1, len(df)):
|
|
on_bar(i, st, open_pos, close)
|
|
if len(equity) == len(trades) + 1:
|
|
equity.append(equity[-1])
|
|
|
|
if st.side is not None:
|
|
close(len(df) - 1, float(df["close"].iloc[-1]), "eod")
|
|
|
|
eq = pd.Series(equity[: len(df)], index=df.index[: len(equity)])
|
|
return build_report(STRATEGY_ID, symbol, tf_label, period_label, trades, eq, initial_balance, params)
|
|
|
|
|
|
def save_reports(report: BacktestReport, out_dir: Path) -> None:
|
|
rows = [
|
|
{
|
|
"side": t.side,
|
|
"open_time": t.open_time,
|
|
"close_time": t.close_time,
|
|
"open_price": t.open_price,
|
|
"close_price": t.close_price,
|
|
"volume": t.volume,
|
|
"profit": t.profit,
|
|
"bars_held": t.bars_held,
|
|
"exit_reason": t.exit_reason,
|
|
}
|
|
for t in report.trades_list
|
|
]
|
|
pd.DataFrame(rows).to_csv(out_dir / "trades.csv", index=False)
|
|
with open(out_dir / "backtest_report.json", "w", encoding="utf-8") as f:
|
|
json.dump(report.to_dict(), f, indent=2, ensure_ascii=False)
|
|
|
|
trades = report.trades_list
|
|
if not trades:
|
|
fig, ax = plt.subplots(figsize=(10, 4))
|
|
ax.text(0.5, 0.5, "No trades in backtest window", ha="center", va="center", fontsize=14)
|
|
ax.axis("off")
|
|
fig.savefig(out_dir / "report.png", dpi=200, bbox_inches="tight")
|
|
plt.close(fig)
|
|
return
|
|
|
|
df = pd.DataFrame(rows)
|
|
df["close_time"] = pd.to_datetime(df["close_time"])
|
|
df = df.sort_values("close_time")
|
|
bal0 = report.params.get("initial_balance", 10_000.0)
|
|
equity = bal0 + df["profit"].cumsum()
|
|
|
|
fig = plt.figure(figsize=(14, 10))
|
|
gs = fig.add_gridspec(3, 2, height_ratios=[2, 1.2, 1.2])
|
|
ax1 = fig.add_subplot(gs[0, :])
|
|
ax1.plot(df["close_time"], equity, lw=1.8)
|
|
ax1.axhline(bal0, color="gray", ls="--")
|
|
ax1.set_title("Equity Curve")
|
|
ax1.grid(alpha=0.3)
|
|
ax2 = fig.add_subplot(gs[1, 0])
|
|
dd = (equity - equity.cummax()) / equity.cummax() * 100
|
|
ax2.fill_between(df["close_time"], dd, 0, color="#d62728", alpha=0.35)
|
|
ax2.set_title("Drawdown %")
|
|
ax2.grid(alpha=0.3)
|
|
ax3 = fig.add_subplot(gs[1, 1])
|
|
df["month"] = df["close_time"].dt.to_period("M")
|
|
monthly = df.groupby("month")["profit"].sum()
|
|
ax3.bar(range(len(monthly)), monthly.values, color=["#2ca02c" if v >= 0 else "#d62728" for v in monthly])
|
|
ax3.set_title("Monthly PnL")
|
|
ax3.axhline(0, color="black", lw=0.6)
|
|
ax4 = fig.add_subplot(gs[2, 0])
|
|
ax4.hist(df["profit"], bins=30, color="#9467bd", alpha=0.85)
|
|
ax4.axvline(0, color="black")
|
|
ax4.set_title("Trade PnL Distribution")
|
|
ax5 = fig.add_subplot(gs[2, 1])
|
|
rc = df["exit_reason"].value_counts()
|
|
ax5.bar(rc.index.astype(str), rc.values, color="#ff7f0e")
|
|
ax5.set_title("Exit Reasons")
|
|
fig.suptitle(
|
|
f"{STRATEGY_ID} — Net ${report.net_profit:,.2f} | Trades {report.total_trades} | "
|
|
f"WR {report.win_rate:.1f}% | PF {report.profit_factor:.2f} | MaxDD {report.max_drawdown_pct:.2f}%",
|
|
fontsize=11,
|
|
)
|
|
fig.tight_layout(rect=[0, 0, 1, 0.96])
|
|
fig.savefig(out_dir / "report.png", dpi=200, bbox_inches="tight")
|
|
plt.close(fig)
|
|
|
|
plt.figure(figsize=(12, 5))
|
|
plt.plot(df["close_time"], equity, lw=2)
|
|
plt.title("Equity Curve")
|
|
plt.grid(alpha=0.3)
|
|
plt.tight_layout()
|
|
plt.savefig(out_dir / "equity_curve.png", dpi=200, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
plt.figure(figsize=(12, 5))
|
|
plt.fill_between(df["close_time"], dd, 0, color="red", alpha=0.3)
|
|
plt.plot(df["close_time"], dd, color="darkred")
|
|
plt.title("Drawdown %")
|
|
plt.grid(alpha=0.3)
|
|
plt.tight_layout()
|
|
plt.savefig(out_dir / "drawdown.png", dpi=200, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
plt.figure(figsize=(12, 5))
|
|
plt.bar(range(len(monthly)), monthly.values, color=["green" if v >= 0 else "red" for v in monthly], alpha=0.75)
|
|
plt.title("Monthly PnL")
|
|
plt.axhline(0, color="black")
|
|
plt.grid(alpha=0.3, axis="y")
|
|
plt.tight_layout()
|
|
plt.savefig(out_dir / "monthly_returns.png", dpi=200, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
plt.figure(figsize=(10, 5))
|
|
plt.hist(df["profit"], bins=40, color="#6a5acd", alpha=0.85)
|
|
plt.axvline(0, color="black")
|
|
plt.title("Per-Trade PnL Distribution")
|
|
plt.tight_layout()
|
|
plt.savefig(out_dir / "pnl_distribution.png", dpi=200, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
if report.exit_reason_breakdown:
|
|
labels = list(report.exit_reason_breakdown.keys())
|
|
counts = [report.exit_reason_breakdown[k]["count"] for k in labels]
|
|
plt.figure(figsize=(8, 5))
|
|
plt.bar(labels, counts, color="#e377c2")
|
|
plt.title("Exit Reason Counts")
|
|
plt.tight_layout()
|
|
plt.savefig(out_dir / "exit_reasons.png", dpi=200, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
|
|
|
|
@dataclass
|
|
class StrategyParams:
|
|
range_start_hour: int = 3
|
|
range_end_hour: int = 6
|
|
close_hour: int = 18
|
|
min_range_pts: float = 5
|
|
order_buffer_pts: float = 1.0
|
|
first_trade_only: bool = False
|
|
allow_long: bool = True
|
|
allow_short: bool = True
|
|
lot_size: float = 0.01
|
|
initial_balance: float = 10_000.0
|
|
|
|
def to_dict(self) -> dict:
|
|
return asdict(self)
|
|
|
|
|
|
def make_params(balance: float) -> StrategyParams:
|
|
return StrategyParams(initial_balance=balance)
|
|
|
|
|
|
|
|
def run_backtest(df, symbol, params: StrategyParams, costs, period_label):
|
|
info = mt5.symbol_info(symbol)
|
|
point = float(info.point) if info else 0.001
|
|
buf = params.order_buffer_pts * point
|
|
day_state: dict = {}
|
|
p = params.to_dict()
|
|
|
|
def on_bar(i, st, open_pos, close):
|
|
ts = df.index[i]
|
|
dk = ts.date().isoformat()
|
|
h = ts.hour
|
|
mid = float(df["open"].iloc[i])
|
|
if st.side and h >= params.close_hour:
|
|
close(i, mid, "eod")
|
|
return
|
|
if dk not in day_state:
|
|
day_state[dk] = {"hi": -np.inf, "lo": np.inf, "built": False, "trades": 0, "range_done": False}
|
|
ds = day_state[dk]
|
|
if params.range_start_hour <= h < params.range_end_hour:
|
|
ds["hi"] = max(ds["hi"], float(df["high"].iloc[i]))
|
|
ds["lo"] = min(ds["lo"], float(df["low"].iloc[i]))
|
|
return
|
|
if not ds["range_done"] and h >= params.range_end_hour:
|
|
ds["range_done"] = True
|
|
if ds["hi"] > ds["lo"] and (ds["hi"] - ds["lo"]) / point >= params.min_range_pts:
|
|
ds["built"] = True
|
|
if not ds["built"] or st.side:
|
|
return
|
|
max_tr = 1 if params.first_trade_only else 2
|
|
if ds["trades"] >= max_tr:
|
|
return
|
|
hi_lvl = ds["hi"] + buf
|
|
lo_lvl = ds["lo"] - buf
|
|
bar_hi = float(df["high"].iloc[i])
|
|
bar_lo = float(df["low"].iloc[i])
|
|
if params.allow_long and bar_hi >= hi_lvl:
|
|
open_pos(i, "BUY", mid)
|
|
st.sl = ds["lo"]
|
|
ds["trades"] += 1
|
|
elif params.allow_short and bar_lo <= lo_lvl:
|
|
open_pos(i, "SELL", mid)
|
|
st.sl = ds["hi"]
|
|
ds["trades"] += 1
|
|
if st.side:
|
|
if st.side == "BUY" and bar_lo <= st.sl:
|
|
close(i, st.sl, "sl")
|
|
elif st.side == "SELL" and bar_hi >= st.sl:
|
|
close(i, st.sl, "sl")
|
|
|
|
return run_single_position(df, symbol, point, costs, params.lot_size, "M1", period_label, p, params.initial_balance, on_bar)
|
|
|
|
|
|
|
|
def parse_args() -> argparse.Namespace:
|
|
p = argparse.ArgumentParser(description=f"{STRATEGY_ID} Python backtest")
|
|
p.add_argument("--symbol", default="USDJPY")
|
|
p.add_argument("--start", default="2021-01-01")
|
|
p.add_argument("--end", default="2026-01-01")
|
|
p.add_argument("--balance", type=float, default=10_000.0)
|
|
return p.parse_args()
|
|
|
|
|
|
def main() -> None:
|
|
args = parse_args()
|
|
out_dir = Path(__file__).resolve().parent
|
|
params = make_params(args.balance)
|
|
if not mt5.initialize():
|
|
raise SystemExit("MetaTrader5 initialize() failed")
|
|
try:
|
|
symbol = resolve_symbol(args.symbol)
|
|
start = datetime.fromisoformat(args.start)
|
|
end = datetime.fromisoformat(args.end)
|
|
period_label = f"{args.start}_{args.end}"
|
|
print(f"Loading {symbol} bars ...")
|
|
df = load_bars(symbol, mt5.TIMEFRAME_M1, start, end)
|
|
costs = CostModel.for_symbol(symbol)
|
|
report = run_backtest(df, symbol, params, costs, period_label)
|
|
save_reports(report, out_dir)
|
|
print(f"Net: ${report.net_profit:,.2f} | Trades: {report.total_trades} | WR: {report.win_rate:.1f}% | PF: {report.profit_factor:.2f}")
|
|
print(f"Saved to {out_dir}")
|
|
finally:
|
|
mt5.shutdown()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|