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XauBot/backtests/backtest_06_stochastic.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

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"""
Backtest SMC + Stochastic Filter
=================================
Base: SMC-Only v4 (100% synced with main_live.py)
Added: Stochastic Oscillator Filter (%K/%D 14,3,3)
Stochastic Filter Logic:
- BUY blocked if Stoch %K > 75 (overbought — price likely to drop)
- SELL blocked if Stoch %K < 25 (oversold — price likely to bounce)
- Stochastic crossover tracked for analysis
Exit: ALL 3 systems unchanged (SmartPositionManager + SmartRiskManager + Time/Trend)
Usage:
python backtests/backtest_stochastic.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")
# Stochastic parameters
STOCH_K_PERIOD = 14
STOCH_D_PERIOD = 3
STOCH_OVERBOUGHT = 75
STOCH_OVERSOLD = 25
# ─── 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"
stoch_k: float = 0.0
stoch_d: float = 0.0
@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
# Stochastic filter stats
stoch_filtered: int = 0
stoch_filtered_buy_overbought: int = 0
stoch_filtered_sell_oversold: int = 0
# ─── Stochastic Calculation ──────────────────────────────────
def calculate_stochastic(df: pl.DataFrame, k_period: int = 14, d_period: int = 3) -> pl.DataFrame:
"""Calculate Stochastic Oscillator %K and %D."""
highs = df["high"].to_list()
lows = df["low"].to_list()
closes = df["close"].to_list()
n = len(closes)
stoch_k = [50.0] * n # default neutral
stoch_d = [50.0] * n
for i in range(k_period - 1, n):
high_max = max(highs[i - k_period + 1 : i + 1])
low_min = min(lows[i - k_period + 1 : i + 1])
if high_max - low_min > 0:
stoch_k[i] = ((closes[i] - low_min) / (high_max - low_min)) * 100
else:
stoch_k[i] = 50.0
# %D = SMA of %K
for i in range(k_period - 1 + d_period - 1, n):
stoch_d[i] = np.mean(stoch_k[i - d_period + 1 : i + 1])
df = df.with_columns([
pl.Series("stoch_k", stoch_k),
pl.Series("stoch_d", stoch_d),
])
return df
# ─── SMC + Stochastic Backtest ───────────────────────────────
class SMCStochasticBacktest:
"""SMC-Only v4 + Stochastic Filter. All exit systems unchanged."""
def __init__(
self,
capital: float = 5000.0,
max_daily_loss_percent: float = 5.0,
max_loss_per_trade_percent: float = 1.0,
base_lot_size: float = 0.01,
max_lot_size: float = 0.02,
recovery_lot_size: float = 0.01,
trend_reversal_threshold: float = 0.75,
max_concurrent_positions: int = 2,
breakeven_pips: float = 30.0,
trail_start_pips: float = 50.0,
trail_step_pips: float = 30.0,
min_profit_to_protect: float = 5.0,
max_drawdown_from_peak: float = 50.0,
trade_cooldown_bars: int = 10,
trend_reversal_mult: float = 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.max_concurrent_positions = max_concurrent_positions
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 + stoch filter)")
except Exception:
print(" [WARN] ML model not loaded — exit ML checks disabled")
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
# ── Session filter (synced) ──
def _get_session_from_time(self, dt: datetime) -> Tuple[str, bool, float]:
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: datetime) -> float:
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: datetime) -> bool:
if dt.tzinfo is None:
dt = dt.replace(tzinfo=ZoneInfo("UTC"))
wib = dt.astimezone(WIB)
if wib.weekday() == 5 and wib.hour >= 4 and wib.minute >= 30:
return True
return False
# ── Lot sizing (synced) ──
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
lot = max(0.01, lot * session_mult)
return round(lot, 2)
# ── Full exit simulation (ALL 3 systems — unchanged from 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 = 0.0
stall_count = 0
reversal_warnings = 0
current_sl = stop_loss
breakeven_moved = 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 = highs[i]
low = lows[i]
close = closes[i]
current_time = times[i]
if direction == "BUY":
current_pips = (close - entry_price) / 0.1
pip_profit_from_entry = (close - entry_price) / 0.1
else:
current_pips = (entry_price - close) / 0.1
pip_profit_from_entry = (entry_price - close) / 0.1
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:
df_slice = df.head(i + 1)
ml_pred = self.ml_model.predict(df_slice, feature_cols)
cached_ml_signal = ml_pred.signal
cached_ml_confidence = 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
profit_change = recent[-1] - recent[0]
momentum = max(-100, min(100, (profit_change / 10) * 50))
profit_growing = momentum > 0
# A) SmartPositionManager
if direction == "BUY" and high >= take_profit:
pips = (take_profit - entry_price) / 0.1
profit = pips * pip_value * lot_size
return profit, pips, ExitReason.TAKE_PROFIT, i, take_profit
elif direction == "SELL" and low <= take_profit:
pips = (entry_price - take_profit) / 0.1
profit = pips * pip_value * lot_size
return profit, 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
profit = pips * pip_value * lot_size
reason = ExitReason.TRAILING_SL if pip_profit_from_entry >= self.trail_start_pips else ExitReason.BREAKEVEN_EXIT
return profit, pips, reason, i, current_sl
elif direction == "SELL" and high >= current_sl:
pips = (entry_price - current_sl) / 0.1
profit = pips * pip_value * lot_size
reason = ExitReason.TRAILING_SL if pip_profit_from_entry >= self.trail_start_pips else ExitReason.BREAKEVEN_EXIT
return profit, pips, reason, i, current_sl
if pip_profit_from_entry >= self.breakeven_pips and not breakeven_moved:
if direction == "BUY":
current_sl = entry_price + 2
else:
current_sl = 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_trail_sl = close - trail_distance
if new_trail_sl > current_sl:
current_sl = new_trail_sl
else:
new_trail_sl = close + trail_distance
if current_sl == 0 or new_trail_sl < current_sl:
current_sl = new_trail_sl
if peak_profit > self.min_profit_to_protect:
drawdown_pct = ((peak_profit - current_profit) / peak_profit) * 100 if peak_profit > 0 else 0
if drawdown_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:
if i >= 20:
ma_fast = np.mean(closes[i-4:i+1])
ma_slow = np.mean(closes[i-19:i+1])
trend = "NEUTRAL"
if ma_fast > ma_slow * 1.001:
trend = "BULLISH"
elif ma_fast < ma_slow * 0.999:
trend = "BEARISH"
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 = 0
should_exit = False
if cached_ml_confidence > 0.75:
if direction == "BUY" and cached_ml_signal == "SELL":
should_exit = True
urgency += 2
elif direction == "SELL" and cached_ml_signal == "BUY":
should_exit = True
urgency += 2
if rsi_val:
if rsi_val > 75 and direction == "BUY":
should_exit = True
urgency += 2
elif rsi_val < 25 and direction == "SELL":
should_exit = True
urgency += 2
if direction == "BUY" and trend == "BEARISH" and mom_dir == "BEARISH":
should_exit = True
urgency += 3
elif direction == "SELL" and trend == "BULLISH" and mom_dir == "BULLISH":
should_exit = 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 > 0:
return current_profit, current_pips, ExitReason.WEEKEND_CLOSE, i, close
elif 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
progress_score = min(40, max(0, progress * 0.4))
momentum_score = ((momentum + 100) / 200) * 30
time_penalty = min(10, bars_since_entry / 4 * 2)
tp_probability = progress_score + momentum_score + 10 - time_penalty
if tp_probability < 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:
is_reversal = (
(direction == "BUY" and cached_ml_signal == "SELL") or
(direction == "SELL" and cached_ml_signal == "BUY")
)
if is_reversal:
return current_profit, current_pips, ExitReason.EARLY_EXIT, i, close
if current_profit < 0:
loss_percent_of_max = abs(current_profit) / self.max_loss_per_trade * 100
if momentum < -30 and loss_percent_of_max >= 30:
return current_profit, current_pips, ExitReason.EARLY_CUT, i, close
is_ml_reversal = False
if direction == "BUY" and cached_ml_signal == "SELL" and cached_ml_confidence >= self.trend_reversal_threshold:
is_ml_reversal = True
reversal_warnings += 1
elif direction == "SELL" and cached_ml_signal == "BUY" and cached_ml_confidence >= self.trend_reversal_threshold:
is_ml_reversal = True
reversal_warnings += 1
loss_moderate = abs(current_profit) > (self.max_loss_per_trade * 0.4)
if is_ml_reversal and current_profit < -8 and loss_moderate:
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 htg <= 1 and htg > 0 and momentum > -40:
pass
else:
return current_profit, current_pips, ExitReason.MAX_LOSS, i, close
if len(profit_history) >= 10:
recent_range = max(profit_history[-10:]) - min(profit_history[-10:])
if recent_range < 3 and current_profit < -15:
stall_count += 1
if stall_count >= 5:
return current_profit, current_pips, ExitReason.STALL, i, close
potential_daily_loss = daily_loss_so_far + abs(min(0, current_profit))
if potential_daily_loss >= self.max_daily_loss_usd:
return current_profit, current_pips, ExitReason.DAILY_LIMIT, i, close
# C) Time-based exit
ml_agrees = (
(direction == "BUY" and cached_ml_signal == "BUY") or
(direction == "SELL" and cached_ml_signal == "SELL")
)
if bars_since_entry >= 16:
if current_profit < 5 and not profit_growing:
if current_profit >= 0:
return current_profit, current_pips, ExitReason.TIMEOUT, i, close
elif current_profit > -15:
return current_profit, current_pips, ExitReason.TIMEOUT, i, close
if bars_since_entry >= 24:
if 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:
recent_closes = closes[i-5:i+1]
mom = recent_closes[-1] - recent_closes[0]
if direction == "BUY" and mom < -reversal_momentum_threshold:
if 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:
if 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)
final_price = closes[final_idx]
if direction == "BUY":
pips = (final_price - entry_price) / 0.1
else:
pips = (entry_price - final_price) / 0.1
profit = pips * pip_value * lot_size
return profit, pips, ExitReason.TIMEOUT, final_idx, final_price
# ── Main backtest run ──
def run(self, df, 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 = 0.0
daily_profit = 0.0
daily_trades = 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.columns]
# Pre-compute stochastic values
stoch_k_list = df["stoch_k"].to_list()
stoch_d_list = df["stoch_d"].to_list()
times = df["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) - 100) if end_date else len(df) - 100
last_trade_idx = -self.trade_cooldown_bars * 2
print(f"\n Running SMC + Stochastic backtest...")
print(f" Stochastic Filter: BUY blocked if K > {STOCH_OVERBOUGHT}, SELL blocked if K < {STOCH_OVERSOLD}")
print(f" Stochastic Period: K={STOCH_K_PERIOD}, D={STOCH_D_PERIOD}")
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 = 0.0
daily_profit = 0.0
daily_trades = 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.head(i + 1)
# Regime check
regime = "normal"
regime_state = None
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 filter
try:
ml_signal = ""
ml_confidence = 0.5
if self.ml_model.fitted and feature_cols:
ml_pred = self.ml_model.predict(df_slice, feature_cols)
ml_signal = ml_pred.signal
ml_confidence = 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
# ═══════════════════════════════════════════════════════
# STOCHASTIC FILTER — the ONLY addition to baseline
# ═══════════════════════════════════════════════════════
current_stoch_k = stoch_k_list[i] if i < len(stoch_k_list) else 50.0
current_stoch_d = stoch_d_list[i] if i < len(stoch_d_list) else 50.0
if smc_signal.signal_type == "BUY":
if current_stoch_k > STOCH_OVERBOUGHT:
stats.stoch_filtered += 1
stats.stoch_filtered_buy_overbought += 1
continue
if smc_signal.signal_type == "SELL":
if current_stoch_k < STOCH_OVERSOLD:
stats.stoch_filtered += 1
stats.stoch_filtered_sell_oversold += 1
continue
# ═══════════════════════════════════════════════════════
# 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:
atr_val = df_slice.tail(1)["atr"].item()
if atr_val is not None and atr_val > 0:
atr_at_entry = atr_val
# Confidence (synced)
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
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
# Execute trade
entry_price = smc_signal.entry_price
take_profit_price = smc_signal.take_profit
stop_loss_price = smc_signal.stop_loss
risk = abs(entry_price - stop_loss_price)
rr = abs(take_profit_price - entry_price) / risk if risk > 0 else 0
profit, pips, exit_reason, exit_idx, exit_price = self._simulate_trade_exit(
df=df, entry_idx=i, direction=smc_signal.signal_type,
entry_price=entry_price, take_profit=take_profit_price,
stop_loss=stop_loss_price, 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=stop_loss_price, take_profit=take_profit_price,
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,
stoch_k=current_stoch_k, stoch_d=current_stoch_d,
)
stats.trades.append(trade)
# Update state
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
drawdown_pct = (peak_capital - capital) / peak_capital * 100
drawdown_usd = peak_capital - capital
if drawdown_pct > stats.max_drawdown:
stats.max_drawdown = drawdown_pct
stats.max_drawdown_usd = drawdown_usd
stats.equity_curve.append(capital)
last_trade_idx = exit_idx
if stats.total_trades % 100 == 0:
print(f" {stats.total_trades} trades processed...")
# Final statistics
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")
win_prob = stats.wins / stats.total_trades
loss_prob = stats.losses / stats.total_trades
stats.expectancy = (win_prob * stats.avg_win) - (loss_prob * stats.avg_loss)
returns = [t.profit_usd for t in stats.trades]
if len(returns) > 1:
avg_return = np.mean(returns)
std_return = np.std(returns)
stats.sharpe_ratio = (avg_return / std_return) * np.sqrt(252) if std_return > 0 else 0
return stats
# ─── XLSX Report ───────────────────────────────────────────────
def generate_xlsx_report(stats: BacktestStats, filepath: str, start_date, end_date):
wb = Workbook()
header_font = Font(name="Calibri", bold=True, size=12, color="FFFFFF")
header_fill = PatternFill(start_color="1F4E79", end_color="1F4E79", fill_type="solid")
subheader_font = Font(name="Calibri", bold=True, size=10)
subheader_fill = PatternFill(start_color="D6E4F0", end_color="D6E4F0", fill_type="solid")
win_fill = PatternFill(start_color="C6EFCE", end_color="C6EFCE", fill_type="solid")
loss_fill = 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
# ═══ SHEET 1: SUMMARY ═══
ws = wb.active
ws.title = "Summary"
ws.sheet_properties.tabColor = "1F4E79"
ws.merge_cells("A1:F1")
ws["A1"] = "XAUBot AI — SMC + Stochastic Filter Backtest"
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')}"
ws["A2"].font = Font(name="Calibri", size=10, italic=True)
ws["A3"] = f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
ws["A3"].font = Font(name="Calibri", size=10, italic=True)
summary_data = [
("Performance Metrics", "", True),
("Total Trades", stats.total_trades, False),
("Wins", stats.wins, False),
("Losses", stats.losses, False),
("Win Rate", f"{stats.win_rate:.1f}%", False),
("Stoch Filtered (Total)", stats.stoch_filtered, False),
(" BUY blocked (overbought)", stats.stoch_filtered_buy_overbought, False),
(" SELL blocked (oversold)", stats.stoch_filtered_sell_oversold, False),
("Avoided (AVOID filter)", stats.avoided_signals, False),
("Recovery Mode Trades", stats.recovery_mode_trades, False),
("Daily Limit Stops", stats.daily_limit_stops, False),
("", "", False),
("Profit - Loss", "", True),
("Total Profit", f"${stats.total_profit:,.2f}", False),
("Total Loss", f"${stats.total_loss:,.2f}", False),
("Net PnL", f"${net_pnl:,.2f}", False),
("Profit Factor", f"{stats.profit_factor:.2f}", False),
("", "", False),
("Risk Metrics", "", True),
("Max Drawdown", f"{stats.max_drawdown:.1f}%", False),
("Max Drawdown ($)", f"${stats.max_drawdown_usd:,.2f}", False),
("Avg Win", f"${stats.avg_win:,.2f}", False),
("Avg Loss", f"${stats.avg_loss:,.2f}", False),
("Avg Trade", f"${stats.avg_trade:,.2f}", False),
("Expectancy", f"${stats.expectancy:,.2f}", False),
("Sharpe Ratio", f"{stats.sharpe_ratio:.2f}", False),
]
row = 5
for label, value, is_header in summary_data:
ws.cell(row=row, column=1, value=label)
ws.cell(row=row, column=2, value=value)
if is_header:
ws.cell(row=row, column=1).font = subheader_font
ws.cell(row=row, column=1).fill = subheader_fill
ws.cell(row=row, column=2).fill = subheader_fill
if label == "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
# Exit Reason Breakdown
exit_counts = {}
for t in stats.trades:
reason = t.exit_reason.value
exit_counts[reason] = exit_counts.get(reason, 0) + 1
ws.cell(row=5, column=4, value="Exit Reasons")
ws.cell(row=5, column=4).font = subheader_font
ws.cell(row=5, column=4).fill = subheader_fill
ws.cell(row=5, column=5).fill = subheader_fill
ws.cell(row=5, column=6).fill = subheader_fill
row = 6
for reason, count in sorted(exit_counts.items(), key=lambda x: -x[1]):
pct = count / stats.total_trades * 100 if stats.total_trades > 0 else 0
ws.cell(row=row, column=4, value=reason)
ws.cell(row=row, column=5, value=count)
ws.cell(row=row, column=6, value=f"{pct:.1f}%")
row += 1
# Session Breakdown
row += 1
ws.cell(row=row, column=4, value="Session Performance")
ws.cell(row=row, column=4).font = subheader_font
ws.cell(row=row, column=4).fill = subheader_fill
for c in range(5, 8):
ws.cell(row=row, column=c).fill = subheader_fill
row += 1
for lbl, col in [("Session", 4), ("Trades", 5), ("WR", 6), ("Net PnL", 7)]:
ws.cell(row=row, column=col, value=lbl).font = Font(bold=True)
row += 1
session_stats = {}
for t in stats.trades:
s = t.session
if s not in session_stats:
session_stats[s] = {"w": 0, "l": 0, "p": 0.0}
if t.result == TradeResult.WIN:
session_stats[s]["w"] += 1
else:
session_stats[s]["l"] += 1
session_stats[s]["p"] += t.profit_usd
for sess, d in sorted(session_stats.items(), key=lambda x: -x[1]["p"]):
total = d["w"] + d["l"]
wr = d["w"] / total * 100 if total > 0 else 0
ws.cell(row=row, column=4, value=sess)
ws.cell(row=row, column=5, value=total)
ws.cell(row=row, column=6, value=f"{wr:.1f}%")
ws.cell(row=row, column=7, value=f"${d['p']:,.2f}")
ws.cell(row=row, column=7).font = Font(color="006100" if d["p"] >= 0 else "9C0006")
row += 1
col_widths = {4: 28, 5: 10, 6: 12, 7: 14}
for c, w in col_widths.items():
ws.column_dimensions[get_column_letter(c)].width = w
# ═══ SHEET 2: TRADE LOG ═══
ws2 = wb.create_sheet("Trade Log")
ws2.sheet_properties.tabColor = "2E75B6"
headers = [
"Ticket", "Entry Time", "Exit Time", "Dir", "Entry", "Exit", "SL", "TP",
"Lot", "Profit ($)", "Pips", "Result", "Exit Reason", "SMC Conf",
"Regime", "Session", "Signal", "BOS", "CHoCH", "FVG", "OB", "ATR", "RR",
"Mode", "Stoch K", "Stoch D",
]
for col, h in enumerate(headers, 1):
cell = ws2.cell(row=1, column=col, value=h)
cell.font = header_font
cell.fill = header_fill
cell.alignment = Alignment(horizontal="center")
for ri, t in enumerate(stats.trades, 2):
vals = [
t.ticket, t.entry_time.strftime("%Y-%m-%d %H:%M"), t.exit_time.strftime("%Y-%m-%d %H:%M"),
t.direction, t.entry_price, t.exit_price, t.stop_loss, t.take_profit,
t.lot_size, round(t.profit_usd, 2), round(t.profit_pips, 1), t.result.value,
t.exit_reason.value, round(t.smc_confidence, 2), t.regime, t.session, t.signal_reason,
"Y" if t.has_bos else "", "Y" if t.has_choch else "", "Y" if t.has_fvg else "",
"Y" if t.has_ob else "", round(t.atr_at_entry, 2), round(t.rr_ratio, 2), t.trading_mode,
round(t.stoch_k, 1), round(t.stoch_d, 1),
]
for ci, v in enumerate(vals, 1):
cell = ws2.cell(row=ri, column=ci, value=v)
cell.border = border
if ci == 10 and isinstance(v, (int, float)):
cell.fill = win_fill if v > 0 else (loss_fill if v < 0 else PatternFill())
if ci == 12:
cell.fill = win_fill if v == "WIN" else (loss_fill if v == "LOSS" else PatternFill())
for col in range(1, len(headers) + 1):
ws2.column_dimensions[get_column_letter(col)].width = max(11, len(headers[col - 1]) + 3)
# ═══ SHEET 3: EQUITY CURVE ═══
ws3 = wb.create_sheet("Equity Curve")
ws3.sheet_properties.tabColor = "548235"
for c, h in enumerate(["Trade #", "Equity", "Drawdown ($)"], 1):
ws3.cell(row=1, column=c, value=h).font = header_font
ws3.cell(row=1, column=c).fill = header_fill
peak = stats.equity_curve[0] if stats.equity_curve else 5000
for idx, eq in enumerate(stats.equity_curve):
if eq > peak:
peak = eq
ws3.cell(row=idx + 2, column=1, value=idx)
ws3.cell(row=idx + 2, column=2, value=round(eq, 2))
ws3.cell(row=idx + 2, column=3, value=round(peak - eq, 2))
if len(stats.equity_curve) > 1:
chart = LineChart()
chart.title = "Equity Curve"
chart.style = 10
chart.y_axis.title = "Equity ($)"
chart.x_axis.title = "Trade #"
chart.width = 30
chart.height = 15
data = Reference(ws3, min_col=2, min_row=1, max_row=len(stats.equity_curve) + 1)
chart.add_data(data, titles_from_data=True)
chart.series[0].graphicalProperties.line.width = 20000
ws3.add_chart(chart, "E2")
# ═══ SHEET 4: DAILY PnL ═══
ws4 = wb.create_sheet("Daily PnL")
ws4.sheet_properties.tabColor = "BF8F00"
daily_pnl = {}
for t in stats.trades:
day = t.entry_time.strftime("%Y-%m-%d")
if day not in daily_pnl:
daily_pnl[day] = {"trades": 0, "wins": 0, "profit": 0.0}
daily_pnl[day]["trades"] += 1
if t.result == TradeResult.WIN:
daily_pnl[day]["wins"] += 1
daily_pnl[day]["profit"] += t.profit_usd
for c, h in enumerate(["Date", "Trades", "Wins", "WR", "Net PnL", "Cumulative"], 1):
ws4.cell(row=1, column=c, value=h).font = header_font
ws4.cell(row=1, column=c).fill = header_fill
cum = 0.0
for ri, (day, d) in enumerate(sorted(daily_pnl.items()), 2):
wr = d["wins"] / d["trades"] * 100 if d["trades"] > 0 else 0
cum += d["profit"]
ws4.cell(row=ri, column=1, value=day)
ws4.cell(row=ri, column=2, value=d["trades"])
ws4.cell(row=ri, column=3, value=d["wins"])
ws4.cell(row=ri, column=4, value=f"{wr:.0f}%")
ws4.cell(row=ri, column=5, value=round(d["profit"], 2))
ws4.cell(row=ri, column=6, value=round(cum, 2))
ws4.cell(row=ri, column=5).fill = win_fill if d["profit"] >= 0 else loss_fill
for c in range(1, 7):
ws4.column_dimensions[get_column_letter(c)].width = 16
wb.save(filepath)
print(f"\n Report saved: {filepath}")
# ─── Log Generator ─────────────────────────────────────────────
def generate_log(stats: BacktestStats, filepath: str, start_date, end_date):
net_pnl = stats.total_profit - stats.total_loss
lines = []
lines.append("=" * 80)
lines.append("XAUBOT AI — SMC + Stochastic Filter Backtest Log")
lines.append("=" * 80)
lines.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
lines.append(f"Period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}")
lines.append(f"Strategy: SMC-Only v4 + Stochastic Filter (K={STOCH_K_PERIOD}, OB={STOCH_OVERBOUGHT}, OS={STOCH_OVERSOLD})")
lines.append("")
lines.append("--- STOCHASTIC FILTER STATS ---")
lines.append(f" Total Blocked: {stats.stoch_filtered}")
lines.append(f" BUY blocked (K>{STOCH_OVERBOUGHT}): {stats.stoch_filtered_buy_overbought}")
lines.append(f" SELL blocked (K<{STOCH_OVERSOLD}): {stats.stoch_filtered_sell_oversold}")
lines.append("")
lines.append("--- PERFORMANCE SUMMARY ---")
lines.append(f" Total Trades: {stats.total_trades}")
lines.append(f" Wins: {stats.wins}")
lines.append(f" Losses: {stats.losses}")
lines.append(f" Win Rate: {stats.win_rate:.1f}%")
lines.append(f" Total Profit: ${stats.total_profit:,.2f}")
lines.append(f" Total Loss: ${stats.total_loss:,.2f}")
lines.append(f" Net PnL: ${net_pnl:,.2f}")
lines.append(f" Profit Factor: {stats.profit_factor:.2f}")
lines.append(f" Max Drawdown: {stats.max_drawdown:.1f}% (${stats.max_drawdown_usd:,.2f})")
lines.append(f" Avg Win: ${stats.avg_win:,.2f}")
lines.append(f" Avg Loss: ${stats.avg_loss:,.2f}")
lines.append(f" Expectancy: ${stats.expectancy:,.2f}")
lines.append(f" Sharpe Ratio: {stats.sharpe_ratio:.2f}")
lines.append(f" Avoided (AVOID): {stats.avoided_signals}")
lines.append(f" Recovery Trades: {stats.recovery_mode_trades}")
lines.append(f" Daily Stops: {stats.daily_limit_stops}")
lines.append("")
lines.append("--- EXIT REASON BREAKDOWN ---")
exit_counts = {}
for t in stats.trades:
r = t.exit_reason.value
exit_counts[r] = exit_counts.get(r, 0) + 1
for reason, count in sorted(exit_counts.items(), key=lambda x: -x[1]):
pct = count / stats.total_trades * 100 if stats.total_trades > 0 else 0
lines.append(f" {reason:20s}: {count:4d} ({pct:5.1f}%)")
lines.append("")
lines.append("--- DIRECTION BREAKDOWN ---")
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)
dwr = dw / len(dt) * 100 if dt else 0
lines.append(f" {d}: {len(dt)} trades, {dwr:.1f}% WR, ${dp:,.2f}")
lines.append("")
lines.append("--- SESSION BREAKDOWN ---")
ss = {}
for t in stats.trades:
if t.session not in ss:
ss[t.session] = {"w": 0, "l": 0, "p": 0.0}
if t.result == TradeResult.WIN:
ss[t.session]["w"] += 1
else:
ss[t.session]["l"] += 1
ss[t.session]["p"] += t.profit_usd
for s, d in sorted(ss.items(), key=lambda x: -x[1]["p"]):
total = d["w"] + d["l"]
wr = d["w"] / total * 100 if total > 0 else 0
lines.append(f" {s:30s}: {total:3d} trades, {wr:5.1f}% WR, ${d['p']:>8,.2f}")
lines.append("")
lines.append("--- SMC COMPONENT ANALYSIS ---")
for cn, attr in [("BOS", "has_bos"), ("CHoCH", "has_choch"), ("FVG", "has_fvg"), ("OB", "has_ob")]:
ct = [t for t in stats.trades if getattr(t, attr)]
cw = sum(1 for t in ct if t.result == TradeResult.WIN)
cp = sum(t.profit_usd for t in ct)
cwr = cw / len(ct) * 100 if ct else 0
lines.append(f" {cn:6s}: {len(ct):3d} trades, {cwr:5.1f}% WR, ${cp:>8,.2f}")
lines.append("")
lines.append("--- TRADE LOG ---")
lines.append(f"{'#':>4} {'Entry Time':>16} {'Dir':>4} {'Entry':>10} {'Exit':>10} {'P/L($)':>8} {'Result':>6} {'Exit Reason':>18} {'StochK':>7} {'StochD':>7} {'Session':>20}")
lines.append("-" * 150)
for idx, t in enumerate(stats.trades, 1):
lines.append(
f"{idx:4d} {t.entry_time.strftime('%Y-%m-%d %H:%M'):>16} {t.direction:>4} "
f"{t.entry_price:>10.2f} {t.exit_price:>10.2f} {t.profit_usd:>8.2f} "
f"{t.result.value:>6} {t.exit_reason.value:>18} {t.stoch_k:>7.1f} {t.stoch_d:>7.1f} "
f"{t.session:>20}"
)
lines.append("\n" + "=" * 80)
lines.append("END OF REPORT")
with open(filepath, "w", encoding="utf-8") as f:
f.write("\n".join(lines))
print(f" Log saved: {filepath}")
# ─── Main ──────────────────────────────────────────────────────
def main():
print("=" * 70)
print("XAUBOT AI — SMC + Stochastic Filter Backtest")
print("Base: SMC-Only v4 (100% synced)")
print(f"Added: Stochastic Filter (K={STOCH_K_PERIOD}, OB>{STOCH_OVERBOUGHT} block BUY, OS<{STOCH_OVERSOLD} block SELL)")
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 XAUUSD M15 historical data...")
df = mt5.get_market_data(symbol="XAUUSD", timeframe="M15", count=50000)
if len(df) == 0:
print("ERROR: No data received")
mt5.disconnect()
return
print(f" Received {len(df)} bars")
times = df["time"].to_list()
print(f" Data 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" [INFO] Adjusted start: {start_date}")
print(f"\n Backtest period: {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}")
print("\nCalculating indicators...")
features = FeatureEngineer()
smc = SMCAnalyzer(swing_length=config.smc.swing_length, ob_lookback=config.smc.ob_lookback)
df = features.calculate_all(df, include_ml_features=True)
df = smc.calculate_all(df)
# Calculate Stochastic
print(" Calculating Stochastic Oscillator...")
df = calculate_stochastic(df, k_period=STOCH_K_PERIOD, d_period=STOCH_D_PERIOD)
regime_detector = MarketRegimeDetector(model_path="models/hmm_regime.pkl")
try:
regime_detector.load()
df = regime_detector.predict(df)
print(" HMM regime loaded")
except Exception:
print(" [WARN] HMM not available")
print(" Indicators calculated")
print(f" ML model loaded (for exit evaluation)")
backtest = SMCStochasticBacktest(
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=df, start_date=start_date, end_date=end_date, initial_capital=5000.0)
net_pnl = stats.total_profit - stats.total_loss
baseline_pnl = 1449.86
print("\n" + "=" * 70)
print("SMC + STOCHASTIC FILTER — RESULTS")
print("=" * 70)
print(f"\n Stochastic Filter Stats:")
print(f" Total blocked: {stats.stoch_filtered}")
print(f" BUY overbought: {stats.stoch_filtered_buy_overbought}")
print(f" SELL oversold: {stats.stoch_filtered_sell_oversold}")
print(f"\n Performance:")
print(f" Total Trades: {stats.total_trades}")
print(f" Wins: {stats.wins}")
print(f" Losses: {stats.losses}")
print(f" Win Rate: {stats.win_rate:.1f}%")
print(f"\n Profit/Loss:")
print(f" Total Profit: ${stats.total_profit:,.2f}")
print(f" Total Loss: ${stats.total_loss:,.2f}")
print(f" Net PnL: ${net_pnl:,.2f}")
print(f" Profit Factor: {stats.profit_factor:.2f}")
print(f"\n Risk Metrics:")
print(f" Max Drawdown: {stats.max_drawdown:.1f}% (${stats.max_drawdown_usd:,.2f})")
print(f" Avg Win: ${stats.avg_win:,.2f}")
print(f" Avg Loss: ${stats.avg_loss:,.2f}")
print(f" Expectancy: ${stats.expectancy:,.2f}")
print(f" Sharpe Ratio: {stats.sharpe_ratio:.2f}")
print(f"\n vs BASELINE:")
print(f" Baseline Net PnL: ${baseline_pnl:,.2f}")
print(f" Improved Net PnL: ${net_pnl:,.2f}")
print(f" Delta: ${net_pnl - baseline_pnl:,.2f}")
print(f"\n Exit Reasons:")
exit_counts = {}
for t in stats.trades:
r = t.exit_reason.value
exit_counts[r] = exit_counts.get(r, 0) + 1
for reason, count in sorted(exit_counts.items(), key=lambda x: -x[1]):
pct = count / stats.total_trades * 100 if stats.total_trades > 0 else 0
print(f" {reason:20s}: {count} ({pct:.1f}%)")
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)
dwr = dw / len(dt) * 100 if dt else 0
print(f" {d}: {len(dt)} trades, {dwr:.1f}% WR, ${dp:,.2f}")
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "06_stochastic_results")
os.makedirs(output_dir, exist_ok=True)
log_path = os.path.join(output_dir, f"stochastic_{timestamp}.log")
xlsx_path = os.path.join(output_dir, f"stochastic_{timestamp}.xlsx")
generate_log(stats, log_path, start_date, end_date)
generate_xlsx_report(stats, xlsx_path, start_date, end_date)
mt5.disconnect()
print("\n" + "=" * 70)
print(f"Output: {output_dir}")
print(f" Log: {os.path.basename(log_path)}")
print(f" Report: {os.path.basename(xlsx_path)}")
print("=" * 70)
print("Backtest complete!")
if __name__ == "__main__":
main()
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