harwinder
8687f98b39
ATR was computed after EMAs so ribbon compression detection was dead code on all timeframes — moved ATR first. Extended M5 swing S/R lookback from 50 to 120 bars. Added four M5-specific filters: min score, MTF agreement, confirmation TF conviction, and SELL quality cap (A-grade M5 shorts enter at dip extremes and lose). Fixed backtester blocked-hour check to use bar close time matching the live bot. Added trade geometry config (spread gate, SL floor, TP floor RR) and primary_tf mismatch guard in backtester. Backtest: 1133 trades/+11.5R → 419 trades/+41.0R, PF 1.03→1.37, DD 21→11R. Train/val split positive in both halves (+19.8R/+21.3R).
247 lines
7.9 KiB
Python
Executable File
247 lines
7.9 KiB
Python
Executable File
"""
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ForexBot2 — Configuration (XAU/USD focused)
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==========================================
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"""
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import Dict, List
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class Timeframe(Enum):
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M1 = "1min"
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M5 = "5min"
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M15 = "15min"
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M30 = "30min"
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H1 = "1h"
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H4 = "4h"
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D1 = "1d"
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W1 = "1wk"
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class SignalStrength(Enum):
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STRONG_BUY = 2
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BUY = 1
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NEUTRAL = 0
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SELL = -1
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STRONG_SELL = -2
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class MarketSession(Enum):
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SYDNEY = "sydney"
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TOKYO = "tokyo"
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LONDON = "london"
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NEW_YORK = "new_york"
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OVERLAP_LONDON_NY = "london_ny_overlap"
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# ── Supported pairs ──────────────────────────────────────────────
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XAUUSD = "XAU/USD"
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PAIR_INFO = {
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XAUUSD: {"display": "XAU/USD", "pip_value": 0.10, "is_jpy_pair": False},
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}
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@dataclass
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class TechnicalConfig:
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"""Technical indicator parameters."""
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ema_fast: int = 9
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ema_medium: int = 21
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ema_slow: int = 50
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ema_trend: int = 200
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rsi_period: int = 14
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rsi_overbought: float = 70.0
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rsi_oversold: float = 30.0
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rsi_neutral_upper: float = 60.0
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rsi_neutral_lower: float = 40.0
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macd_fast: int = 12
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macd_slow: int = 26
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macd_signal: int = 9
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bb_period: int = 20
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bb_std: float = 2.0
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atr_period: int = 14
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adx_period: int = 14
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adx_strong_trend: float = 25.0
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adx_very_strong: float = 50.0
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divergence_lookback: int = 40
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swing_sr_lookback: int = 50
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disabled_indicators: List[str] = field(default_factory=list)
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ema_mode: str = "4ema" # "4ema" (9/21/50/200) or "2ema" (50/200)
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rsi_mode: str = "regime_aware" # "classic" or "regime_aware"
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macd_as_pillar: bool = True # MACD histogram is a scored signal
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# ROC (Rate of Change)
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roc_period: int = 10
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roc_threshold: float = 1.0 # % threshold for strong signal
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# Keltner Channels
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kc_period: int = 20
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kc_atr_period: int = 10
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kc_multiplier: float = 1.5
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# ADX slope (trend acceleration detection)
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adx_slope_lookback: int = 5
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adx_slope_emerging_threshold: float = 3.0
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regime_weights_override: Dict[str, Dict[str, float]] = field(default_factory=dict)
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@dataclass
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class SignalConfig:
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"""Signal scoring & filtering."""
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min_confidence: float = 55.0
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weights: Dict[str, float] = field(default_factory=lambda: {
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"ema_trend": 1.0,
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"rsi": 1.5,
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"bollinger": 1.5,
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"macd": 1.5,
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"keltner": 1.5,
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"roc": 1.5,
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"candlestick": 1.0,
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"divergence": 1.5,
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})
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mtf_agreement_threshold: int = 2 # of 3 timeframes
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cooldown_bars: int = 5
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default_risk_pct: float = 1.0
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default_rr_ratio: float = 2.0
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atr_sl_multiplier: float = 1.5
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min_rr_ratio: float = 0.5
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# TP tightening (May 2026) was tuned on the pre-spread backtester.
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# Re-tested 2026-07-08 with $0.30 spread + gap fills: 0.8 nearly
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# halves total R (+36.1R vs +66.8R, worse in both train/val halves)
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# — smaller wins make the fixed spread a larger share of every TP.
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tp_tightening_factor: float = 1.0
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# ── Trade geometry ────────────────────────────────────────────
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# Round-trip transaction cost estimate in price units (XAU ~$0.30).
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round_trip_cost: float = 0.30
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# Spread gate: reject setups whose TP1 distance is below
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# round_trip_cost × this multiple (caps cost at ~3% of target).
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# Validated 2026-07: small-target trades lose in both data halves
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# on M5 while TP≥$10 trades earn ~+0.08R/trade.
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min_tp_cost_multiple: float = 30.0
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# Fixed SL floor in pips (80 pips = $8 on XAU). 0 disables — SL then
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# floors at ATR × v2_sl_atr_multiplier only.
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min_sl_pips: float = 80.0
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# TP1 floor as a fraction of SL distance (0.5 = historical RR 0.5).
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tp_floor_rr: float = 0.5
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@dataclass
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class NewsConfig:
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"""Economic calendar & sentiment."""
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tracked_currencies: List[str] = field(default_factory=lambda: ["USD", "XAU"])
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min_impact: str = "medium"
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pre_event_buffer_minutes: int = 30
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post_event_buffer_minutes: int = 15
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rss_feeds: List[str] = field(default_factory=lambda: [
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"https://investinglive.com/feed/",
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"https://www.fxstreet.com/rss",
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])
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@dataclass
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class XauStrategyConfig:
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"""XAU/USD strategy config.
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Legacy gates (H4 RSI, H1 MACD, M15 score) removed in favor of
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new momentum indicators (ROC, Keltner squeeze, ADX slope).
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"""
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enabled: bool = True
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rsi_entry_threshold: float = 45.0
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tp_atr_multiplier: float = 1.0
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sl_atr_multiplier: float = 2.0
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min_ema200_tfs: int = 2
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allow_generic_trades: bool = True
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blocked_hours_utc: List[int] = field(default_factory=lambda: [0, 1, 2, 10, 11, 13, 16, 21])
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weekend_filter: bool = True
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min_atr_threshold: float = 1.0
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v2_sl_atr_multiplier: float = 2.0
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buy_threshold_boost: int = 0
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m5_confirmation_bars: int = 0
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h4_rsi_gate: bool = False
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h1_macd_gate: bool = False
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rsi_zone: tuple = (40.0, 65.0)
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allowed_weekdays: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4, 6]) # Mon-Fri + Sun (Sydney open)
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sell_min_quality: List[str] = field(default_factory=lambda: ["A+", "A", "B"])
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# Volatility regimes in which SELL setups are allowed (low/normal-vol
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# shorts lose consistently against gold's macro uptrend).
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sell_vol_regimes: List[str] = field(default_factory=lambda: ["high", "extreme"])
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# ── M5-specific filters ──────────────────────────────────────
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# M5 signals are noisy; require stronger confirmation before
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# generating trade setups. Without these, M5 is breakeven
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# (+0.01R/trade) with 21% drawdown.
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m5_require_mtf: bool = True
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m5_min_abs_score: float = 30.0
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m5_min_confirmation_score: float = 10.0
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# A-grade M5 SELLs enter at the extreme of temporary dips and
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# lose (-0.017R/trade on 163 trades) — maximum conviction at
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# the worst entry. Cap SELL quality so A-grade can't trigger
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# the ATR-based setup generator; B-grade swing setups survive
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# because they catch moderate drops, not extremes.
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m5_sell_max_quality: List[str] = field(default_factory=lambda: ["B", "C"])
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@dataclass
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class FilterConfig:
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"""Regime and session filters for live signals."""
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regime_filter: bool = False
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regime_filter_timeframe: Timeframe = Timeframe.H4
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regime_filter_min_adx: float = 25.0
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session_filter: bool = True
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session_start_hour: int = 3
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session_end_hour: int = 23
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blocked_hours_utc: List[int] = field(default_factory=lambda: [0, 1, 2, 10, 11, 13, 16, 21])
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allowed_weekdays: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4, 6]) # Mon-Fri + Sun (Sydney open)
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@dataclass
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class PlatformConfig:
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"""Top-level configuration."""
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technical: TechnicalConfig = field(default_factory=TechnicalConfig)
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signals: SignalConfig = field(default_factory=SignalConfig)
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news: NewsConfig = field(default_factory=NewsConfig)
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backtest: FilterConfig = field(default_factory=FilterConfig)
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xau_strategy: XauStrategyConfig = field(default_factory=XauStrategyConfig)
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symbol: str = XAUUSD
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display_name: str = "XAU/USD"
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pip_value: float = 0.10
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primary_timeframe: Timeframe = Timeframe.H1
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confirmation_timeframes: List[Timeframe] = field(
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default_factory=lambda: [Timeframe.M15, Timeframe.H4]
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)
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data_lookback_days: int = 60
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twelve_data_api_key: str = "f563b22ffdfd4924955decbe1a8c8701"
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refresh_interval: int = 300
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scan_pairs: List[str] = field(
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default_factory=lambda: [XAUUSD]
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)
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scan_timeframes: List[Timeframe] = field(
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default_factory=lambda: [Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4]
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)
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candle_buffer_seconds: int = 10
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ws_cooldown_seconds: int = 300
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log_level: str = "INFO"
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