gavindiaz/XauBot
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
0f9548e5fb40ed263ccaffe5bf73c49887aa7a9c
XauBot/backtests/backtest_09_h4_zone.py
T
GifariKemal e8355b3f62 feat: add 5 dashboard features — dark mode, trade history, backtests, model insights, alerts 
- Dark mode: class-based theme toggle with localStorage persistence and flash prevention
- Trade History (/trades): paginated table, stats cards, equity curve chart with DB API endpoints
- Backtest Viewer (/backtests): log parser for 35 backtest results, sidebar + detail + comparison tabs
- Model Insights: dashboard card + dialog showing feature importance, regime distribution, training history
- Alert/Signal Log (/alerts): signal stats, filterable table with execution tracking
- API: 8 new endpoints with psycopg2 DB connection pool
- Dark mode sweep across books page, about dialog, and all dashboard components
- Architecture docs rewritten with Mermaid diagrams (23 docs)
- README and FEATURES.md rewritten bilingual (Indonesian + English)
- main_live.py: write model_metrics.json on startup and retrain

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-09 05:46:54 +07:00

1010 lines
46 KiB
Python
Raw Blame History

"""
Backtest A: SMC + H4 Zone Filter
==================================
Base: SMC-Only v4 (100% synced with main_live.py)
Added: H4 Multi-Timeframe Zone Filter
Logic:
- Fetch H4 data, run SMCAnalyzer on H4 to find OB and FVG zones
- For each M15 SMC signal, check if entry price is WITHIN an active H4 zone:
* BUY: price must be in H4 Bullish OB or Bullish FVG (demand zone)
* SELL: price must be in H4 Bearish OB or Bearish FVG (supply zone)
- If NOT in H4 zone → signal rejected
- SL/TP: UNCHANGED from baseline (M15 swing low + 1.5x ATR)
Exit: ALL 3 systems unchanged
Usage:
python backtests/backtest_h4_zone.py
"""
import polars as pl
import pandas as pd
import numpy as np
from datetime import datetime, timedelta, date
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass, field
from enum import Enum
import sys
import os
from zoneinfo import ZoneInfo
from openpyxl import Workbook
from openpyxl.styles import Font, Alignment, PatternFill, Border, Side
from openpyxl.chart import LineChart, Reference
from openpyxl.utils import get_column_letter
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from src.mt5_connector import MT5Connector
from src.smc_polars import SMCAnalyzer, SMCSignal
from src.feature_eng import FeatureEngineer
from src.regime_detector import MarketRegimeDetector, MarketRegime
from src.ml_model import TradingModel
from src.config import get_config
from src.dynamic_confidence import DynamicConfidenceManager, create_dynamic_confidence, MarketQuality
from loguru import logger
logger.remove()
logger.add(sys.stderr, level="WARNING")
WIB = ZoneInfo("Asia/Jakarta")
# H4 zone tolerance (±1.5% price deviation for zone matching ~$42 at $2800)
# H4 zones are narrow ($5-20 wide), need wider tolerance for practical matching
H4_ZONE_TOLERANCE = 0.015
# ─── Enums & Dataclasses ──────────────────────────────────────
class TradeResult(Enum):
WIN = "WIN"
LOSS = "LOSS"
BREAKEVEN = "BREAKEVEN"
class ExitReason(Enum):
TAKE_PROFIT = "take_profit"
SMART_TP = "smart_tp"
PEAK_PROTECT = "peak_protect"
EARLY_EXIT = "early_exit"
EARLY_CUT = "early_cut"
MAX_LOSS = "max_loss"
STALL = "stall"
TREND_REVERSAL = "trend_reversal"
TIMEOUT = "timeout"
WEEKEND_CLOSE = "weekend_close"
TRAILING_SL = "trailing_sl"
BREAKEVEN_EXIT = "breakeven_exit"
DAILY_LIMIT = "daily_limit"
REGIME_DANGER = "regime_danger"
MARKET_SIGNAL = "market_signal"
class TradingMode(Enum):
NORMAL = "normal"
RECOVERY = "recovery"
PROTECTED = "protected"
STOPPED = "stopped"
@dataclass
class SimulatedTrade:
ticket: int
entry_time: datetime
exit_time: datetime
direction: str
entry_price: float
exit_price: float
stop_loss: float
take_profit: float
lot_size: float
profit_usd: float
profit_pips: float
result: TradeResult
exit_reason: ExitReason
smc_confidence: float
regime: str
session: str
signal_reason: str
has_bos: bool = False
has_choch: bool = False
has_fvg: bool = False
has_ob: bool = False
atr_at_entry: float = 0.0
rr_ratio: float = 0.0
trading_mode: str = "normal"
h4_zone_type: str = "none" # "OB", "FVG", "none"
@dataclass
class BacktestStats:
total_trades: int = 0
wins: int = 0
losses: int = 0
total_profit: float = 0.0
total_loss: float = 0.0
max_drawdown: float = 0.0
max_drawdown_usd: float = 0.0
win_rate: float = 0.0
profit_factor: float = 0.0
avg_win: float = 0.0
avg_loss: float = 0.0
avg_trade: float = 0.0
expectancy: float = 0.0
sharpe_ratio: float = 0.0
trades: List[SimulatedTrade] = field(default_factory=list)
equity_curve: List[float] = field(default_factory=list)
avoided_signals: int = 0
daily_limit_stops: int = 0
recovery_mode_trades: int = 0
# H4 zone filter stats
h4_filtered: int = 0
h4_filtered_buy: int = 0
h4_filtered_sell: int = 0
h4_zone_ob_trades: int = 0
h4_zone_fvg_trades: int = 0
# ─── H4 Zone Helper ──────────────────────────────────────────
def extract_h4_zones(df_h4: pl.DataFrame, current_m15_time) -> Dict:
"""
Extract active H4 OB and FVG zones from H4 data.
Only use H4 candles that have CLOSED before current M15 time.
"""
zones = {
"bullish_obs": [],
"bearish_obs": [],
"bullish_fvgs": [],
"bearish_fvgs": [],
}
h4_times = df_h4["time"].to_list()
h4_obs = df_h4["ob"].to_list()
h4_ob_tops = df_h4["ob_top"].to_list()
h4_ob_bottoms = df_h4["ob_bottom"].to_list()
h4_fvg_bulls = df_h4["is_fvg_bull"].to_list()
h4_fvg_bears = df_h4["is_fvg_bear"].to_list()
h4_fvg_tops = df_h4["fvg_top"].to_list()
h4_fvg_bottoms = df_h4["fvg_bottom"].to_list()
h4_closes = df_h4["close"].to_list()
h4_highs = df_h4["high"].to_list()
h4_lows = df_h4["low"].to_list()
n = len(df_h4)
# Scan last 50 H4 candles (~8 days) for active zones
start = max(0, n - 50)
for i in range(start, n):
# Only use closed H4 candles (time + 4h < current M15 time)
if h4_times[i] >= current_m15_time:
break
# Order Blocks — zone invalid only if price BROKE THROUGH (not just touched)
if h4_obs[i] == 1 and h4_ob_tops[i] is not None:
invalidated = False
for j in range(i + 1, min(i + 20, n)):
if h4_times[j] >= current_m15_time:
break
# Bullish OB invalid if price broke BELOW zone bottom
if h4_closes[j] < h4_ob_bottoms[i]:
invalidated = True
break
if not invalidated:
zones["bullish_obs"].append({
"top": h4_ob_tops[i],
"bottom": h4_ob_bottoms[i],
"time": h4_times[i],
})
if h4_obs[i] == -1 and h4_ob_tops[i] is not None:
invalidated = False
for j in range(i + 1, min(i + 20, n)):
if h4_times[j] >= current_m15_time:
break
# Bearish OB invalid if price broke ABOVE zone top
if h4_closes[j] > h4_ob_tops[i]:
invalidated = True
break
if not invalidated:
zones["bearish_obs"].append({
"top": h4_ob_tops[i],
"bottom": h4_ob_bottoms[i],
"time": h4_times[i],
})
# FVGs — invalid only if price CLOSED beyond the gap (fully filled)
if h4_fvg_bulls[i] and h4_fvg_tops[i] is not None:
filled = False
for j in range(i + 1, min(i + 20, n)):
if h4_times[j] >= current_m15_time:
break
# Bullish FVG filled if price closed below gap bottom
if h4_closes[j] < h4_fvg_bottoms[i]:
filled = True
break
if not filled:
zones["bullish_fvgs"].append({
"top": h4_fvg_tops[i],
"bottom": h4_fvg_bottoms[i],
"time": h4_times[i],
})
if h4_fvg_bears[i] and h4_fvg_tops[i] is not None:
filled = False
for j in range(i + 1, min(i + 20, n)):
if h4_times[j] >= current_m15_time:
break
# Bearish FVG filled if price closed above gap top
if h4_closes[j] > h4_fvg_tops[i]:
filled = True
break
if not filled:
zones["bearish_fvgs"].append({
"top": h4_fvg_tops[i],
"bottom": h4_fvg_bottoms[i],
"time": h4_times[i],
})
return zones
def is_price_in_h4_zone(price: float, direction: str, h4_zones: Dict, tolerance: float = H4_ZONE_TOLERANCE) -> Tuple[bool, str]:
"""
Check if price is within an active H4 zone.
Returns (is_in_zone, zone_type).
"""
price_tol = price * tolerance
if direction == "BUY":
# BUY: check demand zones (bullish OB, bullish FVG)
for ob in h4_zones.get("bullish_obs", []):
if ob["bottom"] - price_tol <= price <= ob["top"] + price_tol:
return True, "OB"
for fvg in h4_zones.get("bullish_fvgs", []):
if fvg["bottom"] - price_tol <= price <= fvg["top"] + price_tol:
return True, "FVG"
elif direction == "SELL":
# SELL: check supply zones (bearish OB, bearish FVG)
for ob in h4_zones.get("bearish_obs", []):
if ob["bottom"] - price_tol <= price <= ob["top"] + price_tol:
return True, "OB"
for fvg in h4_zones.get("bearish_fvgs", []):
if fvg["bottom"] - price_tol <= price <= fvg["top"] + price_tol:
return True, "FVG"
return False, "none"
# ─── SMC + H4 Zone Backtest ──────────────────────────────────
class SMCH4ZoneBacktest:
"""SMC-Only v4 + H4 Zone Filter. SL unchanged. All exit systems unchanged."""
def __init__(self, capital=5000.0, max_daily_loss_percent=5.0,
max_loss_per_trade_percent=1.0, base_lot_size=0.01,
max_lot_size=0.02, recovery_lot_size=0.01,
trend_reversal_threshold=0.75, max_concurrent_positions=2,
breakeven_pips=30.0, trail_start_pips=50.0, trail_step_pips=30.0,
min_profit_to_protect=5.0, max_drawdown_from_peak=50.0,
trade_cooldown_bars=10, trend_reversal_mult=0.6):
self.capital = capital
self.max_daily_loss_usd = capital * (max_daily_loss_percent / 100)
self.max_loss_per_trade = capital * (max_loss_per_trade_percent / 100)
self.base_lot_size = base_lot_size
self.max_lot_size = max_lot_size
self.recovery_lot_size = recovery_lot_size
self.trend_reversal_threshold = trend_reversal_threshold
self.breakeven_pips = breakeven_pips
self.trail_start_pips = trail_start_pips
self.trail_step_pips = trail_step_pips
self.min_profit_to_protect = min_profit_to_protect
self.max_drawdown_from_peak = max_drawdown_from_peak
self.trade_cooldown_bars = trade_cooldown_bars
self.trend_reversal_mult = trend_reversal_mult
config = get_config()
self.smc = SMCAnalyzer(swing_length=config.smc.swing_length, ob_lookback=config.smc.ob_lookback)
self.features = FeatureEngineer()
self.dynamic_confidence = create_dynamic_confidence()
self.ml_model = TradingModel(model_path="models/xgboost_model.pkl")
try:
self.ml_model.load()
print(" ML model loaded (for exit evaluation)")
except Exception:
print(" [WARN] ML model not loaded")
self.regime_detector = MarketRegimeDetector(model_path="models/hmm_regime.pkl")
try:
self.regime_detector.load()
except Exception:
print(" [WARN] HMM model not loaded")
self._ticket_counter = 2000000
def _get_session_from_time(self, dt):
if dt.tzinfo is None:
dt = dt.replace(tzinfo=ZoneInfo("UTC"))
wib_time = dt.astimezone(WIB)
hour = wib_time.hour
if 6 <= hour < 15: return "Sydney-Tokyo", True, 0.5
elif 15 <= hour < 16: return "Tokyo-London Overlap", True, 0.75
elif 16 <= hour < 19: return "London Early", True, 0.8
elif 19 <= hour < 24: return "London-NY Overlap (Golden)", True, 1.0
elif 0 <= hour < 4: return "NY Session", True, 0.9
else: return "Off Hours", False, 0.0
def _hours_to_golden(self, dt):
if dt.tzinfo is None: dt = dt.replace(tzinfo=ZoneInfo("UTC"))
wib = dt.astimezone(WIB)
if 19 <= wib.hour < 24: return 0
target = wib.replace(hour=19, minute=0, second=0, microsecond=0)
if wib.hour >= 19: target += timedelta(days=1)
return max(0, (target - wib).total_seconds() / 3600)
def _is_near_weekend_close(self, dt):
if dt.tzinfo is None: dt = dt.replace(tzinfo=ZoneInfo("UTC"))
wib = dt.astimezone(WIB)
return wib.weekday() == 5 and wib.hour >= 4 and wib.minute >= 30
def _calculate_lot_size(self, confidence, regime, trading_mode, session_mult):
if trading_mode == TradingMode.STOPPED: return 0
lot = self.base_lot_size
if trading_mode in (TradingMode.RECOVERY, TradingMode.PROTECTED):
lot = self.recovery_lot_size
else:
if confidence >= 0.65: lot = self.max_lot_size
elif confidence >= 0.55: lot = self.base_lot_size
else: lot = self.recovery_lot_size
if regime.lower() in ["high_volatility", "crisis"]:
lot = self.recovery_lot_size
return round(max(0.01, lot * session_mult), 2)
# ── Full exit simulation (ALL 3 systems — identical to baseline) ──
def _simulate_trade_exit(self, df, entry_idx, direction, entry_price, take_profit, stop_loss, lot_size, daily_loss_so_far, feature_cols, max_bars=100):
pip_value = 10
highs = df["high"].to_list()
lows = df["low"].to_list()
closes = df["close"].to_list()
times = df["time"].to_list()
atr = 12.0
if "atr" in df.columns:
atr_list = df["atr"].to_list()
if entry_idx < len(atr_list) and atr_list[entry_idx] is not None:
atr = atr_list[entry_idx]
reversal_momentum_threshold = atr * self.trend_reversal_mult
min_loss_for_reversal_exit = atr * 0.8
profit_history, price_history = [], []
peak_profit, stall_count, reversal_warnings = 0.0, 0, 0
current_sl, breakeven_moved = stop_loss, False
if direction == "BUY":
target_tp_profit = (take_profit - entry_price) / 0.1 * pip_value * lot_size
else:
target_tp_profit = (entry_price - take_profit) / 0.1 * pip_value * lot_size
cached_ml_signal, cached_ml_confidence = "", 0.5
for i in range(entry_idx + 1, min(entry_idx + max_bars, len(df))):
high, low, close, current_time = highs[i], lows[i], closes[i], times[i]
if direction == "BUY":
current_pips = (close - entry_price) / 0.1
pip_profit_from_entry = current_pips
else:
current_pips = (entry_price - close) / 0.1
pip_profit_from_entry = current_pips
current_profit = current_pips * pip_value * lot_size
profit_history.append(current_profit)
price_history.append(close)
if current_profit > peak_profit: peak_profit = current_profit
bars_since_entry = i - entry_idx
if bars_since_entry % 4 == 0 and self.ml_model.fitted:
try:
ml_pred = self.ml_model.predict(df.head(i + 1), feature_cols)
cached_ml_signal, cached_ml_confidence = ml_pred.signal, ml_pred.confidence
except Exception: pass
momentum = 0.0
if len(profit_history) >= 3:
recent = profit_history[-5:] if len(profit_history) >= 5 else profit_history
momentum = max(-100, min(100, ((recent[-1] - recent[0]) / 10) * 50))
profit_growing = momentum > 0
# A) SmartPositionManager
if direction == "BUY" and high >= take_profit:
pips = (take_profit - entry_price) / 0.1
return pips * pip_value * lot_size, pips, ExitReason.TAKE_PROFIT, i, take_profit
elif direction == "SELL" and low <= take_profit:
pips = (entry_price - take_profit) / 0.1
return pips * pip_value * lot_size, pips, ExitReason.TAKE_PROFIT, i, take_profit
if breakeven_moved and current_sl > 0:
if direction == "BUY" and low <= current_sl:
pips = (current_sl - entry_price) / 0.1
reason = ExitReason.TRAILING_SL if pip_profit_from_entry >= self.trail_start_pips else ExitReason.BREAKEVEN_EXIT
return pips * pip_value * lot_size, pips, reason, i, current_sl
elif direction == "SELL" and high >= current_sl:
pips = (entry_price - current_sl) / 0.1
reason = ExitReason.TRAILING_SL if pip_profit_from_entry >= self.trail_start_pips else ExitReason.BREAKEVEN_EXIT
return pips * pip_value * lot_size, pips, reason, i, current_sl
if pip_profit_from_entry >= self.breakeven_pips and not breakeven_moved:
current_sl = entry_price + 2 if direction == "BUY" else entry_price - 2
breakeven_moved = True
if pip_profit_from_entry >= self.trail_start_pips:
trail_distance = self.trail_step_pips * 0.1
if direction == "BUY":
new_sl = close - trail_distance
if new_sl > current_sl: current_sl = new_sl
else:
new_sl = close + trail_distance
if current_sl == 0 or new_sl < current_sl: current_sl = new_sl
if peak_profit > self.min_profit_to_protect:
dd_pct = ((peak_profit - current_profit) / peak_profit) * 100 if peak_profit > 0 else 0
if dd_pct > self.max_drawdown_from_peak:
return current_profit, current_pips, ExitReason.PEAK_PROTECT, i, close
if bars_since_entry % 5 == 0 and bars_since_entry >= 5 and i >= 20:
ma_fast = np.mean(closes[i-4:i+1])
ma_slow = np.mean(closes[i-19:i+1])
trend = "BULLISH" if ma_fast > ma_slow * 1.001 else ("BEARISH" if ma_fast < ma_slow * 0.999 else "NEUTRAL")
roc = (closes[i] / closes[max(0,i-4)] - 1) * 100
mom_dir = "BULLISH" if roc > 0.3 else ("BEARISH" if roc < -0.3 else "NEUTRAL")
rsi_val = None
if "rsi" in df.columns:
rsi_list = df["rsi"].to_list()
if i < len(rsi_list): rsi_val = rsi_list[i]
urgency, should_exit = 0, False
if cached_ml_confidence > 0.75:
if (direction == "BUY" and cached_ml_signal == "SELL") or (direction == "SELL" and cached_ml_signal == "BUY"):
should_exit, urgency = True, urgency + 2
if rsi_val:
if (rsi_val > 75 and direction == "BUY") or (rsi_val < 25 and direction == "SELL"):
should_exit, urgency = True, urgency + 2
if (direction == "BUY" and trend == "BEARISH" and mom_dir == "BEARISH") or \
(direction == "SELL" and trend == "BULLISH" and mom_dir == "BULLISH"):
should_exit, urgency = True, urgency + 3
if should_exit and current_profit > self.min_profit_to_protect / 2:
return current_profit, current_pips, ExitReason.MARKET_SIGNAL, i, close
if urgency >= 7 and current_profit > 0:
return current_profit, current_pips, ExitReason.MARKET_SIGNAL, i, close
if self._is_near_weekend_close(current_time):
if current_profit > -10:
return current_profit, current_pips, ExitReason.WEEKEND_CLOSE, i, close
# B) SmartRiskManager
if current_profit >= 15:
if current_profit >= 40: return current_profit, current_pips, ExitReason.SMART_TP, i, close
if current_profit >= 25 and momentum < -30: return current_profit, current_pips, ExitReason.SMART_TP, i, close
if peak_profit > 30 and current_profit < peak_profit * 0.6: return current_profit, current_pips, ExitReason.PEAK_PROTECT, i, close
if current_profit >= 20:
progress = (current_profit / target_tp_profit) * 100 if target_tp_profit > 0 else 0
tp_prob = min(40, max(0, progress * 0.4)) + ((momentum + 100) / 200) * 30 + 10 - min(10, bars_since_entry / 4 * 2)
if tp_prob < 25: return current_profit, current_pips, ExitReason.SMART_TP, i, close
if 5 <= current_profit < 15:
if momentum < -50 and cached_ml_confidence >= 0.65:
if (direction == "BUY" and cached_ml_signal == "SELL") or (direction == "SELL" and cached_ml_signal == "BUY"):
return current_profit, current_pips, ExitReason.EARLY_EXIT, i, close
if current_profit < 0:
loss_pct = abs(current_profit) / self.max_loss_per_trade * 100
if momentum < -30 and loss_pct >= 30:
return current_profit, current_pips, ExitReason.EARLY_CUT, i, close
is_ml_rev = False
if (direction == "BUY" and cached_ml_signal == "SELL" and cached_ml_confidence >= self.trend_reversal_threshold) or \
(direction == "SELL" and cached_ml_signal == "BUY" and cached_ml_confidence >= self.trend_reversal_threshold):
is_ml_rev = True
reversal_warnings += 1
if is_ml_rev and current_profit < -8 and abs(current_profit) > self.max_loss_per_trade * 0.4:
return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close
if reversal_warnings >= 3 and current_profit < -10:
return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close
if current_profit <= -(self.max_loss_per_trade * 0.50):
htg = self._hours_to_golden(current_time)
if not (htg <= 1 and htg > 0 and momentum > -40):
return current_profit, current_pips, ExitReason.MAX_LOSS, i, close
if len(profit_history) >= 10:
if max(profit_history[-10:]) - min(profit_history[-10:]) < 3 and current_profit < -15:
stall_count += 1
if stall_count >= 5: return current_profit, current_pips, ExitReason.STALL, i, close
if daily_loss_so_far + abs(min(0, current_profit)) >= self.max_daily_loss_usd:
return current_profit, current_pips, ExitReason.DAILY_LIMIT, i, close
# C) Time-based
if bars_since_entry >= 16 and current_profit < 5 and not profit_growing and current_profit > -15:
return current_profit, current_pips, ExitReason.TIMEOUT, i, close
if bars_since_entry >= 24 and (current_profit < 10 or not profit_growing):
return current_profit, current_pips, ExitReason.TIMEOUT, i, close
if bars_since_entry >= 32:
return current_profit, current_pips, ExitReason.TIMEOUT, i, close
if bars_since_entry > 10:
mom = closes[i] - closes[i-5]
if direction == "BUY" and mom < -reversal_momentum_threshold and current_profit < -min_loss_for_reversal_exit:
return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close
elif direction == "SELL" and mom > reversal_momentum_threshold and current_profit < -min_loss_for_reversal_exit:
return current_profit, current_pips, ExitReason.TREND_REVERSAL, i, close
final_idx = min(entry_idx + max_bars - 1, len(df) - 1)
fp = closes[final_idx]
pips = (fp - entry_price) / 0.1 if direction == "BUY" else (entry_price - fp) / 0.1
return pips * pip_value * lot_size, pips, ExitReason.TIMEOUT, final_idx, fp
# ── Main backtest run ──
def run(self, df_m15, df_h4, start_date=None, end_date=None, initial_capital=5000.0):
stats = BacktestStats()
capital = initial_capital
peak_capital = initial_capital
stats.equity_curve.append(capital)
daily_loss, daily_profit, daily_trades = 0.0, 0.0, 0
consecutive_losses = 0
trading_mode = TradingMode.NORMAL
current_date = None
feature_cols = []
if self.ml_model.fitted and self.ml_model.feature_names:
feature_cols = [f for f in self.ml_model.feature_names if f in df_m15.columns]
times = df_m15["time"].to_list()
start_idx = next((i for i, t in enumerate(times) if t >= start_date), 100) if start_date else 100
end_idx = next((i for i, t in enumerate(times) if t > end_date), len(df_m15) - 100) if end_date else len(df_m15) - 100
last_trade_idx = -self.trade_cooldown_bars * 2
# Cache H4 zones (update every 16 M15 bars = 4 hours)
cached_h4_zones = None
cached_h4_bar = -100
print(f"\n Running SMC + H4 Zone Filter backtest...")
print(f" H4 zones: OB + FVG (unmitigated/unfilled only)")
print(f" Zone tolerance: ±{H4_ZONE_TOLERANCE*100:.2f}%")
print(f" SL: Unchanged (M15 baseline)")
print(f" Date range: {times[start_idx]} to {times[end_idx - 1]}")
print(f" Total bars: {end_idx - start_idx}")
for i in range(start_idx, end_idx):
if i - last_trade_idx < self.trade_cooldown_bars:
continue
current_time = times[i]
# Daily reset
trade_date = current_time.date() if hasattr(current_time, 'date') else current_time
if current_date is None or trade_date != current_date:
daily_loss, daily_profit, daily_trades = 0.0, 0.0, 0
current_date = trade_date
if consecutive_losses < 2: trading_mode = TradingMode.NORMAL
if trading_mode == TradingMode.STOPPED: continue
session_name, can_trade, lot_mult = self._get_session_from_time(current_time)
if not can_trade: continue
if hasattr(current_time, 'weekday') and current_time.weekday() >= 5: continue
df_slice = df_m15.head(i + 1)
# Regime check
regime = "normal"
try:
if self.regime_detector.fitted:
regime_state = self.regime_detector.get_current_state(df_slice)
if regime_state:
regime = regime_state.regime.value
if regime_state.regime == MarketRegime.CRISIS: continue
if regime_state.recommendation == "SLEEP": continue
except Exception: pass
# DynamicConfidence AVOID
ml_signal, ml_confidence = "", 0.5
try:
if self.ml_model.fitted and feature_cols:
ml_pred = self.ml_model.predict(df_slice, feature_cols)
ml_signal, ml_confidence = ml_pred.signal, ml_pred.confidence
market_analysis = self.dynamic_confidence.analyze_market(
session=session_name, regime=regime, volatility="medium",
trend_direction=regime, has_smc_signal=True,
ml_signal=ml_signal, ml_confidence=ml_confidence)
if market_analysis.quality == MarketQuality.AVOID:
stats.avoided_signals += 1
continue
except Exception: pass
# SMC Signal
try:
smc_signal = self.smc.generate_signal(df_slice)
except Exception: continue
if smc_signal is None: continue
# ═══════════════════════════════════════════════════════
# H4 ZONE FILTER — update zones every 16 bars (4h)
# ═══════════════════════════════════════════════════════
if i - cached_h4_bar >= 16 or cached_h4_zones is None:
cached_h4_zones = extract_h4_zones(df_h4, current_time)
cached_h4_bar = i
in_zone, zone_type = is_price_in_h4_zone(
smc_signal.entry_price, smc_signal.signal_type, cached_h4_zones
)
if not in_zone:
stats.h4_filtered += 1
if smc_signal.signal_type == "BUY":
stats.h4_filtered_buy += 1
else:
stats.h4_filtered_sell += 1
continue
if zone_type == "OB":
stats.h4_zone_ob_trades += 1
elif zone_type == "FVG":
stats.h4_zone_fvg_trades += 1
# ═══════════════════════════════════════════════════════
# SMC details
recent_df = df_slice.tail(10)
recent_bos = recent_df["bos"].to_list() if "bos" in df_slice.columns else []
recent_choch = recent_df["choch"].to_list() if "choch" in df_slice.columns else []
recent_fvg_bull = recent_df["is_fvg_bull"].to_list() if "is_fvg_bull" in df_slice.columns else []
recent_fvg_bear = recent_df["is_fvg_bear"].to_list() if "is_fvg_bear" in df_slice.columns else []
recent_obs = recent_df["ob"].to_list() if "ob" in df_slice.columns else []
has_bos = 1 in recent_bos or -1 in recent_bos
has_choch = 1 in recent_choch or -1 in recent_choch
has_fvg = any(recent_fvg_bull) or any(recent_fvg_bear)
has_ob = 1 in recent_obs or -1 in recent_obs
atr_at_entry = 12.0
if "atr" in df_slice.columns:
v = df_slice.tail(1)["atr"].item()
if v and v > 0: atr_at_entry = v
confidence = smc_signal.confidence
ml_agrees = (smc_signal.signal_type == "BUY" and ml_signal == "BUY") or \
(smc_signal.signal_type == "SELL" and ml_signal == "SELL")
if ml_agrees: confidence = (smc_signal.confidence + ml_confidence) / 2
if regime == "high_volatility": confidence *= 0.9
lot_size = self._calculate_lot_size(confidence, regime, trading_mode, lot_mult)
if lot_size <= 0: continue
if trading_mode == TradingMode.RECOVERY: stats.recovery_mode_trades += 1
entry_price = smc_signal.entry_price
tp = smc_signal.take_profit
sl = smc_signal.stop_loss
risk = abs(entry_price - sl)
rr = abs(tp - entry_price) / risk if risk > 0 else 0
profit, pips, exit_reason, exit_idx, exit_price = self._simulate_trade_exit(
df=df_m15, entry_idx=i, direction=smc_signal.signal_type,
entry_price=entry_price, take_profit=tp, stop_loss=sl,
lot_size=lot_size, daily_loss_so_far=daily_loss, feature_cols=feature_cols)
self._ticket_counter += 1
result = TradeResult.WIN if profit > 0 else (TradeResult.LOSS if profit < 0 else TradeResult.BREAKEVEN)
trade = SimulatedTrade(
ticket=self._ticket_counter, entry_time=current_time,
exit_time=times[exit_idx] if exit_idx < len(times) else times[-1],
direction=smc_signal.signal_type, entry_price=entry_price, exit_price=exit_price,
stop_loss=sl, take_profit=tp, lot_size=lot_size,
profit_usd=profit, profit_pips=pips, result=result, exit_reason=exit_reason,
smc_confidence=confidence, regime=regime, session=session_name,
signal_reason=smc_signal.reason, has_bos=has_bos, has_choch=has_choch,
has_fvg=has_fvg, has_ob=has_ob, atr_at_entry=atr_at_entry,
rr_ratio=rr, trading_mode=trading_mode.value, h4_zone_type=zone_type)
stats.trades.append(trade)
stats.total_trades += 1
daily_trades += 1
capital += profit
if profit > 0:
stats.wins += 1
stats.total_profit += profit
daily_profit += profit
consecutive_losses = 0
if trading_mode == TradingMode.RECOVERY: trading_mode = TradingMode.NORMAL
else:
stats.losses += 1
stats.total_loss += abs(profit)
daily_loss += abs(profit)
consecutive_losses += 1
if daily_loss >= self.max_daily_loss_usd:
trading_mode = TradingMode.STOPPED
stats.daily_limit_stops += 1
elif consecutive_losses >= 3 or daily_loss >= self.max_daily_loss_usd * 0.6:
trading_mode = TradingMode.PROTECTED
elif consecutive_losses >= 2:
trading_mode = TradingMode.RECOVERY
if capital > peak_capital: peak_capital = capital
dd_pct = (peak_capital - capital) / peak_capital * 100
dd_usd = peak_capital - capital
if dd_pct > stats.max_drawdown:
stats.max_drawdown = dd_pct
stats.max_drawdown_usd = dd_usd
stats.equity_curve.append(capital)
last_trade_idx = exit_idx
if stats.total_trades % 50 == 0:
print(f" {stats.total_trades} trades processed...")
if stats.total_trades > 0:
stats.win_rate = stats.wins / stats.total_trades * 100
stats.avg_win = stats.total_profit / stats.wins if stats.wins > 0 else 0
stats.avg_loss = stats.total_loss / stats.losses if stats.losses > 0 else 0
stats.avg_trade = (stats.total_profit - stats.total_loss) / stats.total_trades
stats.profit_factor = stats.total_profit / stats.total_loss if stats.total_loss > 0 else float("inf")
wp = stats.wins / stats.total_trades
lp = stats.losses / stats.total_trades
stats.expectancy = (wp * stats.avg_win) - (lp * stats.avg_loss)
returns = [t.profit_usd for t in stats.trades]
if len(returns) > 1:
stats.sharpe_ratio = (np.mean(returns) / np.std(returns)) * np.sqrt(252) if np.std(returns) > 0 else 0
return stats
# ─── Report & Log generators ─────────────────────────────────
def generate_xlsx_report(stats, filepath, start_date, end_date, variant_name):
wb = Workbook()
hf = Font(name="Calibri", bold=True, size=12, color="FFFFFF")
hfill = PatternFill(start_color="1F4E79", end_color="1F4E79", fill_type="solid")
sf = Font(name="Calibri", bold=True, size=10)
sfill = PatternFill(start_color="D6E4F0", end_color="D6E4F0", fill_type="solid")
wfill = PatternFill(start_color="C6EFCE", end_color="C6EFCE", fill_type="solid")
lfill = PatternFill(start_color="FFC7CE", end_color="FFC7CE", fill_type="solid")
border = Border(left=Side(style="thin"), right=Side(style="thin"), top=Side(style="thin"), bottom=Side(style="thin"))
net_pnl = stats.total_profit - stats.total_loss
ws = wb.active
ws.title = "Summary"
ws.merge_cells("A1:F1")
ws["A1"] = f"XAUBot AI — {variant_name}"
ws["A1"].font = Font(name="Calibri", bold=True, size=16, color="1F4E79")
ws["A2"] = f"Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}"
data = [
("Performance", "", True), ("Total Trades", stats.total_trades, False),
("Wins", stats.wins, False), ("Losses", stats.losses, False),
("Win Rate", f"{stats.win_rate:.1f}%", False), ("", "", False),
("H4 Zone Filter", "", True),
(" Total filtered", stats.h4_filtered, False),
(" BUY filtered", stats.h4_filtered_buy, False),
(" SELL filtered", stats.h4_filtered_sell, False),
(" Trades in OB zone", stats.h4_zone_ob_trades, False),
(" Trades in FVG zone", stats.h4_zone_fvg_trades, False),
("", "", False), ("Profit - Loss", "", True),
("Total Profit", f"${stats.total_profit:,.2f}", False),
("Total Loss", f"${stats.total_loss:,.2f}", False),
("Net PnL", f"${net_pnl:,.2f}", False),
("Profit Factor", f"{stats.profit_factor:.2f}", False),
("", "", False), ("Risk Metrics", "", True),
("Max Drawdown", f"{stats.max_drawdown:.1f}%", False),
("Max DD ($)", f"${stats.max_drawdown_usd:,.2f}", False),
("Avg Win", f"${stats.avg_win:,.2f}", False),
("Avg Loss", f"${stats.avg_loss:,.2f}", False),
("Expectancy", f"${stats.expectancy:,.2f}", False),
("Sharpe Ratio", f"{stats.sharpe_ratio:.2f}", False),
]
row = 5
for lbl, val, hdr in data:
ws.cell(row=row, column=1, value=lbl)
ws.cell(row=row, column=2, value=val)
if hdr:
ws.cell(row=row, column=1).font = sf
ws.cell(row=row, column=1).fill = sfill
ws.cell(row=row, column=2).fill = sfill
if lbl == "Net PnL":
ws.cell(row=row, column=2).font = Font(bold=True, color="006100" if net_pnl > 0 else "9C0006")
row += 1
ws.column_dimensions["A"].width = 28
ws.column_dimensions["B"].width = 18
# Trade Log
ws2 = wb.create_sheet("Trade Log")
headers = ["Ticket","Entry Time","Exit Time","Dir","Entry","Exit","SL","TP",
"Lot","Profit ($)","Pips","Result","Exit Reason","Conf","Regime","Session","H4 Zone"]
for c, h in enumerate(headers, 1):
cell = ws2.cell(row=1, column=c, value=h)
cell.font = hf; cell.fill = hfill
for ri, t in enumerate(stats.trades, 2):
vals = [t.ticket, t.entry_time.strftime("%Y-%m-%d %H:%M"), t.exit_time.strftime("%Y-%m-%d %H:%M"),
t.direction, t.entry_price, t.exit_price, t.stop_loss, t.take_profit,
t.lot_size, round(t.profit_usd,2), round(t.profit_pips,1), t.result.value,
t.exit_reason.value, round(t.smc_confidence,2), t.regime, t.session, t.h4_zone_type]
for ci, v in enumerate(vals, 1):
cell = ws2.cell(row=ri, column=ci, value=v)
cell.border = border
if ci == 10 and isinstance(v, (int,float)):
cell.fill = wfill if v > 0 else (lfill if v < 0 else PatternFill())
# Equity Curve
ws3 = wb.create_sheet("Equity Curve")
for c, h in enumerate(["Trade #","Equity"], 1):
ws3.cell(row=1, column=c, value=h).font = hf; ws3.cell(row=1, column=c).fill = hfill
for idx, eq in enumerate(stats.equity_curve):
ws3.cell(row=idx+2, column=1, value=idx)
ws3.cell(row=idx+2, column=2, value=round(eq,2))
if len(stats.equity_curve) > 1:
chart = LineChart(); chart.title = "Equity Curve"; chart.width = 30; chart.height = 15
chart.add_data(Reference(ws3, min_col=2, min_row=1, max_row=len(stats.equity_curve)+1), titles_from_data=True)
ws3.add_chart(chart, "D2")
# Daily PnL
ws4 = wb.create_sheet("Daily PnL")
for c, h in enumerate(["Date","Trades","Wins","WR","Net PnL","Cumulative"], 1):
ws4.cell(row=1, column=c, value=h).font = hf; ws4.cell(row=1, column=c).fill = hfill
dpnl = {}
for t in stats.trades:
d = t.entry_time.strftime("%Y-%m-%d")
if d not in dpnl: dpnl[d] = {"t":0,"w":0,"p":0.0}
dpnl[d]["t"] += 1
if t.result == TradeResult.WIN: dpnl[d]["w"] += 1
dpnl[d]["p"] += t.profit_usd
cum = 0.0
for ri, (d, v) in enumerate(sorted(dpnl.items()), 2):
wr = v["w"]/v["t"]*100 if v["t"]>0 else 0
cum += v["p"]
for ci, val in enumerate([d, v["t"], v["w"], f"{wr:.0f}%", round(v["p"],2), round(cum,2)], 1):
ws4.cell(row=ri, column=ci, value=val)
wb.save(filepath)
print(f"\n Report saved: {filepath}")
def generate_log(stats, filepath, start_date, end_date, variant_name):
net_pnl = stats.total_profit - stats.total_loss
lines = [
"=" * 80, f"XAUBOT AI — {variant_name}", "=" * 80,
f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
f"Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}", "",
"--- H4 ZONE FILTER STATS ---",
f" Total filtered: {stats.h4_filtered}",
f" BUY filtered: {stats.h4_filtered_buy}",
f" SELL filtered: {stats.h4_filtered_sell}",
f" Trades in OB zone: {stats.h4_zone_ob_trades}",
f" Trades in FVG zone: {stats.h4_zone_fvg_trades}", "",
"--- PERFORMANCE ---",
f" Trades: {stats.total_trades} | Wins: {stats.wins} | Losses: {stats.losses}",
f" Win Rate: {stats.win_rate:.1f}% | PF: {stats.profit_factor:.2f}",
f" Net PnL: ${net_pnl:,.2f} | Sharpe: {stats.sharpe_ratio:.2f}",
f" Max DD: {stats.max_drawdown:.1f}% (${stats.max_drawdown_usd:,.2f})",
f" Avg Win: ${stats.avg_win:,.2f} | Avg Loss: ${stats.avg_loss:,.2f}", "",
"--- EXIT REASONS ---",
]
ec = {}
for t in stats.trades:
ec[t.exit_reason.value] = ec.get(t.exit_reason.value, 0) + 1
for r, c in sorted(ec.items(), key=lambda x: -x[1]):
lines.append(f" {r:20s}: {c:4d} ({c/stats.total_trades*100:.1f}%)")
lines.append("")
lines.append("--- DIRECTION ---")
for d in ["BUY", "SELL"]:
dt = [t for t in stats.trades if t.direction == d]
dw = sum(1 for t in dt if t.result == TradeResult.WIN)
dp = sum(t.profit_usd for t in dt)
lines.append(f" {d}: {len(dt)} trades, {dw/len(dt)*100:.1f}% WR, ${dp:,.2f}" if dt else f" {d}: 0 trades")
lines.append("")
lines.append("--- H4 ZONE TYPE ---")
for zt in ["OB", "FVG"]:
zt_trades = [t for t in stats.trades if t.h4_zone_type == zt]
zt_w = sum(1 for t in zt_trades if t.result == TradeResult.WIN)
zt_p = sum(t.profit_usd for t in zt_trades)
zt_wr = zt_w / len(zt_trades) * 100 if zt_trades else 0
lines.append(f" {zt:4s}: {len(zt_trades)} trades, {zt_wr:.1f}% WR, ${zt_p:,.2f}")
with open(filepath, "w", encoding="utf-8") as f:
f.write("\n".join(lines))
print(f" Log saved: {filepath}")
# ─── Main ──────────────────────────────────────────────────────
def main():
VARIANT = "SMC + H4 Zone Filter (SL unchanged)"
print("=" * 70)
print(f"XAUBOT AI — {VARIANT}")
print("Base: SMC-Only v4 | Added: H4 OB/FVG zone entry filter")
print("=" * 70)
config = get_config()
mt5 = MT5Connector(login=config.mt5_login, password=config.mt5_password,
server=config.mt5_server, path=config.mt5_path)
mt5.connect()
print(f"\nConnected to MT5")
print("Fetching M15 data...")
df_m15 = mt5.get_market_data(symbol="XAUUSD", timeframe="M15", count=50000)
print(f" M15: {len(df_m15)} bars")
print("Fetching H4 data...")
df_h4 = mt5.get_market_data(symbol="XAUUSD", timeframe="H4", count=3000)
print(f" H4: {len(df_h4)} bars")
times = df_m15["time"].to_list()
print(f" M15 range: {times[0]} to {times[-1]}")
end_date = datetime.now()
start_date = datetime(2025, 8, 1)
data_start = times[0]
if hasattr(data_start, 'replace') and data_start.tzinfo:
start_date = start_date.replace(tzinfo=data_start.tzinfo)
end_date = end_date.replace(tzinfo=data_start.tzinfo)
if data_start > start_date:
start_date = data_start + timedelta(days=5)
print(f"\n Backtest period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}")
print("\nCalculating M15 indicators...")
features = FeatureEngineer()
smc_m15 = SMCAnalyzer(swing_length=config.smc.swing_length, ob_lookback=config.smc.ob_lookback)
df_m15 = features.calculate_all(df_m15, include_ml_features=True)
df_m15 = smc_m15.calculate_all(df_m15)
print("Calculating H4 SMC zones...")
smc_h4 = SMCAnalyzer(swing_length=5, fvg_min_gap_pips=5.0, ob_lookback=10)
df_h4 = smc_h4.calculate_all(df_h4)
h4_bull_obs = (df_h4["ob"] == 1).sum()
h4_bear_obs = (df_h4["ob"] == -1).sum()
h4_bull_fvg = df_h4["is_fvg_bull"].sum()
h4_bear_fvg = df_h4["is_fvg_bear"].sum()
print(f" H4 OBs: {h4_bull_obs} bullish, {h4_bear_obs} bearish")
print(f" H4 FVGs: {h4_bull_fvg} bullish, {h4_bear_fvg} bearish")
regime_detector = MarketRegimeDetector(model_path="models/hmm_regime.pkl")
try:
regime_detector.load()
df_m15 = regime_detector.predict(df_m15)
print(" HMM regime loaded")
except Exception:
print(" [WARN] HMM not available")
backtest = SMCH4ZoneBacktest(capital=5000.0, max_daily_loss_percent=5.0,
max_loss_per_trade_percent=1.0, base_lot_size=0.01, max_lot_size=0.02,
recovery_lot_size=0.01, breakeven_pips=30.0, trail_start_pips=50.0,
trail_step_pips=30.0, min_profit_to_protect=5.0, max_drawdown_from_peak=50.0,
trade_cooldown_bars=10, trend_reversal_mult=0.6)
stats = backtest.run(df_m15=df_m15, df_h4=df_h4, start_date=start_date, end_date=end_date, initial_capital=5000.0)
net_pnl = stats.total_profit - stats.total_loss
baseline = 1449.86
print("\n" + "=" * 70)
print(f"{VARIANT} — RESULTS")
print("=" * 70)
print(f"\n H4 Zone Filter:")
print(f" Filtered: {stats.h4_filtered} (BUY: {stats.h4_filtered_buy}, SELL: {stats.h4_filtered_sell})")
print(f" OB trades: {stats.h4_zone_ob_trades}")
print(f" FVG trades: {stats.h4_zone_fvg_trades}")
print(f"\n Performance:")
print(f" Trades: {stats.total_trades} | WR: {stats.win_rate:.1f}%")
print(f" Net PnL: ${net_pnl:,.2f} | PF: {stats.profit_factor:.2f}")
print(f" Max DD: {stats.max_drawdown:.1f}% | Sharpe: {stats.sharpe_ratio:.2f}")
print(f" Avg Win: ${stats.avg_win:,.2f} | Avg Loss: ${stats.avg_loss:,.2f}")
print(f"\n vs BASELINE: ${net_pnl - baseline:,.2f}")
print(f"\n Direction:")
for d in ["BUY", "SELL"]:
dt = [t for t in stats.trades if t.direction == d]
dw = sum(1 for t in dt if t.result == TradeResult.WIN)
dp = sum(t.profit_usd for t in dt)
print(f" {d}: {len(dt)} trades, {dw/len(dt)*100:.1f}% WR, ${dp:,.2f}" if dt else f" {d}: 0 trades")
print(f"\n Exit Reasons:")
ec = {}
for t in stats.trades:
ec[t.exit_reason.value] = ec.get(t.exit_reason.value, 0) + 1
for r, c in sorted(ec.items(), key=lambda x: -x[1]):
print(f" {r:20s}: {c} ({c/stats.total_trades*100:.1f}%)" if stats.total_trades > 0 else "")
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
out_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "09_h4_zone_results")
os.makedirs(out_dir, exist_ok=True)
generate_log(stats, os.path.join(out_dir, f"h4_zone_{ts}.log"), start_date, end_date, VARIANT)
generate_xlsx_report(stats, os.path.join(out_dir, f"h4_zone_{ts}.xlsx"), start_date, end_date, VARIANT)
mt5.disconnect()
print(f"\n{'='*70}\nOutput: {out_dir}\n{'='*70}\nBacktest complete!")
if __name__ == "__main__":
main()
Reference in New Issue View Git Blame Copy Permalink