ForexBot2/config.py

"""
ForexBot2 — Configuration (USD/JPY focus)
==========================================
Single-pair signal & backtest configuration.
All knobs that govern indicator behavior, signal scoring, news handling,
and the walk-forward backtester live here.
"""

import os
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional


class Timeframe(Enum):
    M1 = "1min"
    M5 = "5min"
    M15 = "15min"
    M30 = "30min"
    H1 = "1h"
    H4 = "4h"
    D1 = "1d"
    W1 = "1wk"


class SignalStrength(Enum):
    STRONG_BUY = 2
    BUY = 1
    NEUTRAL = 0
    SELL = -1
    STRONG_SELL = -2


class MarketSession(Enum):
    SYDNEY = "sydney"
    TOKYO = "tokyo"
    LONDON = "london"
    NEW_YORK = "new_york"
    OVERLAP_LONDON_NY = "london_ny_overlap"


# ── Single Focus: USD/JPY ─────────────────────────────────────────
USDJPY = "USDJPY=X"
DISPLAY = "USD/JPY"
PIP_VALUE = 0.01  # JPY pairs use 0.01 per pip


@dataclass
class TechnicalConfig:
    """Technical indicator parameters."""
    ema_fast: int = 9
    ema_medium: int = 21
    ema_slow: int = 50
    ema_trend: int = 200

    rsi_period: int = 14
    rsi_overbought: float = 70.0
    rsi_oversold: float = 30.0
    rsi_neutral_upper: float = 60.0
    rsi_neutral_lower: float = 40.0

    macd_fast: int = 12
    macd_slow: int = 26
    macd_signal: int = 9

    bb_period: int = 20
    bb_std: float = 2.0

    atr_period: int = 14

    stoch_k: int = 14
    stoch_d: int = 3
    stoch_overbought: float = 80.0
    stoch_oversold: float = 20.0

    adx_period: int = 14
    adx_strong_trend: float = 25.0
    adx_very_strong: float = 50.0

    ichimoku_tenkan: int = 9
    ichimoku_kijun: int = 26
    ichimoku_senkou_b: int = 52

    volume_ma_period: int = 20
    pivot_lookback: int = 1
    divergence_lookback: int = 14

    # Ablation control — names listed here are skipped during analyze().
    # Use lowercase keys: ema_trend, rsi, macd, bollinger, stochastic,
    # adx, ichimoku, divergence, candlestick, pivot, atr, volume.
    #
    # Defaults disable indicators that empirically hurt P&L on USD/JPY across
    # 2024-trend / 2023-chop / 2026-recent ablation sweeps (see project memory):
    #   - pivot: net +9.5% to disable across 3 windows
    #   - candlestick: net +3.4% to disable
    disabled_indicators: List[str] = field(
        default_factory=lambda: ["pivot", "candlestick"]
    )


ALL_INDICATORS: List[str] = [
    "ema_trend", "rsi", "macd", "bollinger", "stochastic",
    "adx", "ichimoku", "divergence", "candlestick", "pivot",
]


@dataclass
class SignalConfig:
    """Signal scoring & filtering."""
    min_confidence: float = 55.0

    weights: Dict[str, float] = field(default_factory=lambda: {
        "trend": 0.25,
        "momentum": 0.20,
        "volatility": 0.15,
        "price_action": 0.15,
        "multi_timeframe": 0.15,
        "sentiment": 0.10,
    })

    mtf_agreement_threshold: int = 2  # of 3 timeframes
    cooldown_bars: int = 5

    default_risk_pct: float = 1.0
    default_rr_ratio: float = 2.0
    atr_sl_multiplier: float = 1.5  # SL distance = 1.5 × ATR


@dataclass
class NewsConfig:
    """Economic calendar & sentiment."""
    tracked_currencies: List[str] = field(default_factory=lambda: ["USD", "JPY"])
    min_impact: str = "medium"
    pre_event_buffer_minutes: int = 30
    post_event_buffer_minutes: int = 15

    rss_feeds: List[str] = field(default_factory=lambda: [
        "https://www.forexlive.com/feed/",
        "https://www.fxstreet.com/rss",
    ])


@dataclass
class BacktestConfig:
    """Walk-forward backtest configuration."""
    # Data window — None means "all available history"
    start_date: Optional[str] = None       # "YYYY-MM-DD"
    end_date: Optional[str] = None         # "YYYY-MM-DD"
    max_bars: int = 5000                   # Twelve Data ceiling per request

    # Account & sizing
    initial_balance: float = 10_000.0
    risk_per_trade_pct: float = 1.0        # % of equity risked per trade

    # Execution realism
    use_spread_cost: bool = True
    slippage_pips: float = 0.5             # added to spread on entry & exit
    one_position_only: bool = True
    sl_first_on_tie: bool = True           # if SL & TP both inside bar, SL hits first

    # Signal gates
    min_trade_grade: str = "B"             # "A+", "A", "B", "C", "D", "F"
    min_abs_score: float = 0.0             # |final_score| must exceed this to enter

    # Sanity-test knob — flip BUY↔SELL to see if the engine has reversed edge
    invert_signal: bool = False

    # Regime gate — only trade when a higher-TF trend is confirmed
    regime_filter: bool = False
    regime_filter_timeframe: Timeframe = Timeframe.H4
    regime_filter_min_adx: float = 25.0

    # Session gate — only trade during a specified UTC hour window.
    # 2026 diagnostic backtest showed 07-15 UTC (London + NY) had
    # 47% WR / +127 pips while off-hours were 0% WR / -571 pips.
    session_filter: bool = False
    session_start_hour: int = 7   # UTC, inclusive
    session_end_hour: int = 16    # UTC, exclusive — so trades fire on 07..15

    # Auto-toggle: enable session filter ONLY when recent realised trades
    # show adverse expectancy (mean pnl_pct over the last N closed trades
    # below threshold). Releases automatically once expectancy recovers.
    # Designed so trending regimes keep all hours, while adverse/chop
    # regimes auto-restrict to liquid sessions.
    auto_session_filter: bool = False
    auto_session_lookback_trades: int = 10
    auto_session_threshold_pct: float = -0.3

    # Parallelism (used by Optimizer; single-run backtest is sequential)
    parallel_workers: int = field(default_factory=lambda: os.cpu_count() or 4)

    # Optimizer
    optimize: bool = False
    param_grid: Dict[str, list] = field(default_factory=lambda: {
        # 2 × 2 × 3 × 2 × 2 = 48 combos
        "atr_sl_multiplier": [1.5, 2.5],
        "default_rr_ratio":  [1.5, 2.5],
        "min_abs_score":     [15.0, 35.0, 60.0],
        "min_trade_grade":   ["B", "A"],
        "invert_signal":     [False, True],
    })

    # Cache directory for fetched OHLCV (parquet)
    cache_dir: str = ".cache"


@dataclass
class PlatformConfig:
    """Top-level configuration."""
    technical: TechnicalConfig = field(default_factory=TechnicalConfig)
    signals: SignalConfig = field(default_factory=SignalConfig)
    news: NewsConfig = field(default_factory=NewsConfig)
    backtest: BacktestConfig = field(default_factory=BacktestConfig)

    symbol: str = USDJPY
    display_name: str = DISPLAY
    pip_value: float = PIP_VALUE

    primary_timeframe: Timeframe = Timeframe.H1
    confirmation_timeframes: List[Timeframe] = field(
        default_factory=lambda: [Timeframe.M15, Timeframe.H4]
    )

    data_lookback_days: int = 60  # enough for 200 EMA on H1

    twelve_data_api_key: str = "f563b22ffdfd4924955decbe1a8c8701"

    refresh_interval: int = 300  # 5 min cache TTL

    log_level: str = "INFO"
    log_signals_to_file: bool = True
    signal_log_path: str = "signals.log"