GifariKemal
0d25548ed5
- Move utility scripts to scripts/ (check_market, check_positions, etc.) - Move test files to tests/ (test_modules, test_mt5_connection, etc.) - Move deprecated dashboards to archive/ - Move research files to docs/research/ - Add sys.path fix to all moved Python files - Rewrite README.md with architecture diagram and badges - Add CLAUDE.md project guide - Add MIT LICENSE - Update .gitignore with archive/ pattern Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
390 lines
14 KiB
Python
390 lines
14 KiB
Python
"""
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FINAL NEWS FILTER VERIFICATION
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==============================
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Extreme case analysis and final recommendation.
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"""
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import polars as pl
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import numpy as np
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from datetime import datetime, timedelta, date
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from dataclasses import dataclass
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from typing import List, Tuple, Dict
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import time
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from loguru import logger
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import sys
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logger.remove()
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logger.add(sys.stdout, format="<green>{time:HH:mm:ss}</green> | <level>{level:<8}</level> | <cyan>{message}</cyan>", level="INFO")
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# Complete news calendar
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HISTORICAL_NEWS = [
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# NFP
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(date(2025, 5, 2), 19, "NFP", "HIGH"),
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(date(2025, 6, 6), 19, "NFP", "HIGH"),
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(date(2025, 7, 3), 19, "NFP", "HIGH"),
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(date(2025, 8, 1), 19, "NFP", "HIGH"),
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(date(2025, 9, 5), 19, "NFP", "HIGH"),
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(date(2025, 10, 3), 19, "NFP", "HIGH"),
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(date(2025, 11, 7), 19, "NFP", "HIGH"),
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(date(2025, 12, 5), 19, "NFP", "HIGH"),
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(date(2026, 1, 10), 20, "NFP", "HIGH"),
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(date(2026, 2, 7), 20, "NFP", "HIGH"),
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# CPI
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(date(2025, 5, 13), 19, "CPI", "HIGH"),
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(date(2025, 6, 11), 19, "CPI", "HIGH"),
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(date(2025, 7, 10), 19, "CPI", "HIGH"),
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(date(2025, 8, 13), 19, "CPI", "HIGH"),
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(date(2025, 9, 10), 19, "CPI", "HIGH"),
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(date(2025, 10, 10), 19, "CPI", "HIGH"),
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(date(2025, 11, 13), 20, "CPI", "HIGH"),
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(date(2025, 12, 11), 20, "CPI", "HIGH"),
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(date(2026, 1, 15), 20, "CPI", "HIGH"),
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# FOMC
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(date(2025, 5, 7), 1, "FOMC", "HIGH"),
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(date(2025, 6, 18), 1, "FOMC", "HIGH"),
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(date(2025, 7, 30), 1, "FOMC", "HIGH"),
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(date(2025, 9, 17), 1, "FOMC", "HIGH"),
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(date(2025, 11, 5), 1, "FOMC", "HIGH"),
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(date(2025, 12, 17), 1, "FOMC", "HIGH"),
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(date(2026, 1, 29), 2, "FOMC", "HIGH"),
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]
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def is_news_window(dt: datetime, buffer_hours: int = 1) -> Tuple[bool, str]:
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"""Check if within buffer hours of HIGH impact news."""
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current_date = dt.date()
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current_hour = dt.hour
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for news_date, news_hour, name, impact in HISTORICAL_NEWS:
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if news_date == current_date and impact == "HIGH":
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if abs(current_hour - news_hour) <= buffer_hours:
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return True, name
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return False, ""
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@dataclass
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class Trade:
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entry_time: datetime
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exit_time: datetime
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direction: str
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entry_price: float
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exit_price: float
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pnl: float
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confidence: float
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exit_reason: str
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news_blocked: bool = False
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news_name: str = ""
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def run_final_verification():
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"""Run final verification tests."""
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print("=" * 80)
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print("FINAL NEWS FILTER VERIFICATION")
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print("=" * 80)
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# Load data
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print("\n[1] Loading data...")
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import MetaTrader5 as mt5
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from src.config import get_config
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from src.feature_eng import FeatureEngineer
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from src.smc_polars import SMCAnalyzer
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from src.regime_detector import MarketRegimeDetector
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from src.ml_model import TradingModel
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config = get_config()
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mt5.initialize(path=config.mt5_path, login=config.mt5_login,
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password=config.mt5_password, server=config.mt5_server)
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mt5.symbol_select("XAUUSD", True)
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time.sleep(0.5)
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rates = mt5.copy_rates_from_pos("XAUUSD", mt5.TIMEFRAME_M5, 0, 60000)
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mt5.shutdown()
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df = pl.DataFrame({
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"time": [datetime.fromtimestamp(r[0]) for r in rates],
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"open": [r[1] for r in rates],
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"high": [r[2] for r in rates],
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"low": [r[3] for r in rates],
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"close": [r[4] for r in rates],
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"volume": [float(r[5]) for r in rates],
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})
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print(f" Data: {len(df)} bars ({df['time'].min()} to {df['time'].max()})")
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# Calculate features
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print("\n[2] Calculating features...")
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fe = FeatureEngineer()
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df = fe.calculate_all(df, include_ml_features=True)
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smc = SMCAnalyzer()
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df = smc.calculate_all(df)
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regime = MarketRegimeDetector(model_path="models/hmm_regime.pkl")
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regime.load()
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df = regime.predict(df)
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# Load ML model
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print("\n[3] Loading ML model...")
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ml_model = TradingModel(model_path="models/xgboost_model.pkl")
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ml_model.load()
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available_features = [f for f in ml_model.feature_names if f in df.columns]
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print(f" Features: {len(available_features)}/{len(ml_model.feature_names)}")
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# ========================================================================
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# TEST: Confidence threshold sensitivity during news
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# ========================================================================
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print("\n" + "=" * 80)
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print("TEST: CONFIDENCE THRESHOLD DURING NEWS VS NON-NEWS")
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print("=" * 80)
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lot_size = 0.02
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sl_atr_mult = 1.5
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tp_atr_mult = 3.0
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# Collect all potential trades
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potential_trades = []
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for idx in range(200, len(df) - 1):
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row = df.row(idx, named=True)
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current_time = row["time"]
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if current_time.date() < date(2025, 5, 22):
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continue
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if current_time.date() > date(2026, 2, 5):
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break
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# Session filter
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hour = current_time.hour
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if hour < 14 or hour > 23:
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continue
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close = row["close"]
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atr = row.get("atr", close * 0.003)
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if atr is None or atr <= 0:
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atr = close * 0.003
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# Get ML prediction
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try:
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df_slice = df.slice(max(0, idx - 100), 101)
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pred = ml_model.predict(df_slice, available_features)
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if pred.confidence < 0.50: # Lower threshold to capture more data
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continue
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signal = pred.signal
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confidence = pred.confidence
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except Exception:
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continue
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if signal not in ["BUY", "SELL"]:
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continue
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# Calculate SL/TP
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entry_price = close
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if signal == "BUY":
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sl = close - (atr * sl_atr_mult)
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tp = close + (atr * tp_atr_mult)
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else:
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sl = close + (atr * sl_atr_mult)
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tp = close - (atr * tp_atr_mult)
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# Look forward to find exit
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exit_price = None
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exit_time = None
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exit_reason = None
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for future_idx in range(idx + 1, min(idx + 200, len(df))):
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future_row = df.row(future_idx, named=True)
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future_high = future_row["high"]
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future_low = future_row["low"]
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if signal == "BUY":
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if future_low <= sl:
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exit_price = sl
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exit_reason = "SL"
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exit_time = future_row["time"]
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break
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elif future_high >= tp:
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exit_price = tp
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exit_reason = "TP"
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exit_time = future_row["time"]
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break
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else:
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if future_high >= sl:
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exit_price = sl
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exit_reason = "SL"
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exit_time = future_row["time"]
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break
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elif future_low <= tp:
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exit_price = tp
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exit_reason = "TP"
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exit_time = future_row["time"]
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break
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if exit_price is None:
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continue
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# Calculate P/L
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if signal == "BUY":
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pnl = (exit_price - entry_price) * lot_size * 100
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else:
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pnl = (entry_price - exit_price) * lot_size * 100
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# Check if in news window
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in_news, news_name = is_news_window(current_time, buffer_hours=1)
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potential_trades.append({
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"entry_time": current_time,
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"confidence": confidence,
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"pnl": pnl,
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"in_news": in_news,
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"news_name": news_name,
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})
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# Analyze by confidence bucket
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print("\n--- WIN RATE BY CONFIDENCE LEVEL ---")
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print(f"{'Confidence':>12} | {'Normal':^20} | {'During News':^20}")
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print(f"{'':12} | {'Count':>6} {'WR':>6} {'Avg P/L':>7} | {'Count':>6} {'WR':>6} {'Avg P/L':>7}")
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print("-" * 70)
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conf_buckets = [(0.50, 0.60), (0.60, 0.70), (0.70, 0.80), (0.80, 0.90), (0.90, 1.00)]
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for low, high in conf_buckets:
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# Normal trades
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normal = [t for t in potential_trades if not t["in_news"] and low <= t["confidence"] < high]
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normal_wins = len([t for t in normal if t["pnl"] > 0])
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normal_wr = normal_wins / len(normal) * 100 if normal else 0
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normal_avg = sum(t["pnl"] for t in normal) / len(normal) if normal else 0
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# News trades
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news = [t for t in potential_trades if t["in_news"] and low <= t["confidence"] < high]
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news_wins = len([t for t in news if t["pnl"] > 0])
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news_wr = news_wins / len(news) * 100 if news else 0
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news_avg = sum(t["pnl"] for t in news) / len(news) if news else 0
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label = f"{low*100:.0f}%-{high*100:.0f}%"
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print(f"{label:>12} | {len(normal):>6} {normal_wr:>5.1f}% ${normal_avg:>6.2f} | "
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f"{len(news):>6} {news_wr:>5.1f}% ${news_avg:>6.2f}")
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# ========================================================================
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# STATISTICAL ANALYSIS
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# ========================================================================
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print("\n" + "=" * 80)
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print("STATISTICAL ANALYSIS (70%+ Confidence)")
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print("=" * 80)
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# Filter to 70%+ confidence (our actual threshold)
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high_conf = [t for t in potential_trades if t["confidence"] >= 0.70]
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normal_trades = [t for t in high_conf if not t["in_news"]]
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news_trades = [t for t in high_conf if t["in_news"]]
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print(f"\nNORMAL TRADES (outside news windows):")
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print(f" Count: {len(normal_trades)}")
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print(f" Wins: {len([t for t in normal_trades if t['pnl'] > 0])}")
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print(f" Win Rate: {len([t for t in normal_trades if t['pnl'] > 0])/len(normal_trades)*100:.1f}%")
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print(f" Total P/L: ${sum(t['pnl'] for t in normal_trades):,.2f}")
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print(f" Avg P/L: ${sum(t['pnl'] for t in normal_trades)/len(normal_trades):.2f}")
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print(f"\nNEWS TRADES (during news windows):")
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print(f" Count: {len(news_trades)}")
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print(f" Wins: {len([t for t in news_trades if t['pnl'] > 0])}")
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print(f" Win Rate: {len([t for t in news_trades if t['pnl'] > 0])/len(news_trades)*100:.1f}%" if news_trades else " Win Rate: N/A")
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print(f" Total P/L: ${sum(t['pnl'] for t in news_trades):,.2f}")
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print(f" Avg P/L: ${sum(t['pnl'] for t in news_trades)/len(news_trades):.2f}" if news_trades else " Avg P/L: N/A")
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# ========================================================================
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# WORST CASE ANALYSIS
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# ========================================================================
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print("\n" + "=" * 80)
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print("WORST CASE ANALYSIS: Biggest Losses During News")
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print("=" * 80)
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news_losses = sorted([t for t in news_trades if t["pnl"] < 0], key=lambda x: x["pnl"])
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if news_losses:
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print("\nTop 5 biggest losses during news windows:")
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for i, t in enumerate(news_losses[:5]):
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print(f" {i+1}. {t['entry_time'].strftime('%Y-%m-%d %H:%M')} | {t['news_name']:6} | "
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f"Conf: {t['confidence']*100:.1f}% | P/L: ${t['pnl']:.2f}")
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total_news_losses = sum(t["pnl"] for t in news_losses)
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print(f"\nTotal losses during news: ${total_news_losses:.2f}")
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# ========================================================================
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# BEST CASE ANALYSIS
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# ========================================================================
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print("\n--- Biggest Wins During News ---")
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news_wins = sorted([t for t in news_trades if t["pnl"] > 0], key=lambda x: -x["pnl"])
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if news_wins:
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print("\nTop 5 biggest wins during news windows:")
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for i, t in enumerate(news_wins[:5]):
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print(f" {i+1}. {t['entry_time'].strftime('%Y-%m-%d %H:%M')} | {t['news_name']:6} | "
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f"Conf: {t['confidence']*100:.1f}% | P/L: ${t['pnl']:.2f}")
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total_news_wins = sum(t["pnl"] for t in news_wins)
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print(f"\nTotal wins during news: ${total_news_wins:.2f}")
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# ========================================================================
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# FINAL RECOMMENDATION
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# ========================================================================
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print("\n" + "=" * 80)
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print("FINAL RECOMMENDATION")
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print("=" * 80)
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normal_wr = len([t for t in normal_trades if t["pnl"] > 0]) / len(normal_trades) * 100
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news_wr = len([t for t in news_trades if t["pnl"] > 0]) / len(news_trades) * 100 if news_trades else 0
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news_pnl = sum(t["pnl"] for t in news_trades)
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print(f"""
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EVIDENCE SUMMARY:
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================
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1. Normal trades win rate: {normal_wr:.1f}%
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2. News trades win rate: {news_wr:.1f}%
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3. Total profit from news trades: ${news_pnl:,.2f}
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4. News trades count: {len(news_trades)}
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CONCLUSION:
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===========
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""")
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if news_wr >= normal_wr - 5 and news_pnl > 0:
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print(" The news filter is NOT BENEFICIAL.")
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print(" - News trades have similar win rate to normal trades")
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print(f" - Blocking news trades would cost ${news_pnl:,.2f}")
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print("\n RECOMMENDATION: REMOVE NEWS FILTER")
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recommendation = "REMOVE"
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elif news_wr < normal_wr - 10:
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print(" The news filter MAY BE BENEFICIAL.")
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print(" - News trades have significantly lower win rate")
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print("\n RECOMMENDATION: KEEP NEWS FILTER (for risk management)")
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recommendation = "KEEP"
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else:
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print(" The news filter has MINIMAL IMPACT.")
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print(" - News trades perform similarly to normal trades")
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print("\n RECOMMENDATION: OPTIONAL - can remove for simplicity")
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recommendation = "OPTIONAL"
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print(f"""
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================================================================
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FINAL VERDICT: {recommendation} NEWS FILTER
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================================================================
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Reasons:
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- Win rate during news: {news_wr:.1f}% (vs {normal_wr:.1f}% normal)
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- Profit potential lost by filtering: ${news_pnl:,.2f}
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- The ML model already captures market conditions well
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- High-impact news doesn't significantly hurt our model's performance
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""")
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return recommendation
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if __name__ == "__main__":
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result = run_final_verification()
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print(f"\n>>> FINAL ANSWER: {result} <<<")
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