Community Scripts

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 02:37:57
# Model            : Gradient Boosting
# Feature Eng.     : EMA (9,21), RSI 14 + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/EURUSD_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # --- EMA 9 and EMA 21 (required) ---
    ema_9 = close.ewm(span=9, adjust=False).mean()
    ema_21 = close.ewm(span=21, adjust=False).mean()
    df["ema_9"] = ema_9
    df["ema_21"] = ema_21
    df["dm_ema_9"] = (close - ema_9) / ema_9
    df["dm_ema_21"] = (close - ema_21) / ema_21

    # EMA crossover signal: positive when fast > slow
    df["ema_cross"] = ema_9 - ema_21
    # Crossover direction change (sign flip)
    df["ema_cross_signal"] = np.sign(df["ema_cross"])
    df["ema_cross_prev"] = df["ema_cross_signal"].shift(1)
    df["ema_cross_flip"] = (df["ema_cross_signal"] != df["ema_cross_prev"]).astype(float)

    # --- RSI 14 (required) ---
    delta = close.diff()
    gain = delta.clip(lower=0)
    loss = -delta.clip(upper=0)
    avg_gain = gain.ewm(com=13, adjust=False).mean()
    avg_loss = loss.ewm(com=13, adjust=False).mean()
    rs = avg_gain / (avg_loss + 1e-10)
    rsi_14 = 100 - (100 / (1 + rs))
    df["rsi_14"] = rsi_14
    # RSI normalised to [-1, 1] range
    df["rsi_norm"] = (rsi_14 - 50) / 50
    # RSI overbought/oversold flags
    df["rsi_ob"] = np.where(rsi_14 > 70, 1.0, 0.0)
    df["rsi_os"] = np.where(rsi_14 < 30, 1.0, 0.0)

    # --- Additional momentum and volatility features ---

    # EMA 50 for trend context
    ema_50 = close.ewm(span=50, adjust=False).mean()
    df["ema_50"] = ema_50
    df["dm_ema_50"] = (close - ema_50) / ema_50

    # Price momentum: rate of change over multiple horizons
    df["roc_4"] = close.pct_change(4)
    df["roc_8"] = close.pct_change(8)
    df["roc_16"] = close.pct_change(16)

    # ATR (Average True Range) for volatility
    tr = pd.concat([
        high - low,
        (high - close.shift(1)).abs(),
        (low - close.shift(1)).abs()
    ], axis=1).max(axis=1)
    atr_14 = tr.ewm(span=14, adjust=False).mean()
    df["atr_14"] = atr_14
    # Normalised ATR
    df["natr_14"] = atr_14 / close

    # Bollinger Bands (20-period, 2 std)
    bb_mid = close.rolling(20).mean()
    bb_std = close.rolling(20).std()
    bb_upper = bb_mid + 2 * bb_std
    bb_lower = bb_mid - 2 * bb_std
    df["bb_pct"] = (close - bb_lower) / (bb_upper - bb_lower + 1e-10)
    df["bb_width"] = (bb_upper - bb_lower) / (bb_mid + 1e-10)
    # BB squeeze: narrow bands signal potential breakout
    df["bb_squeeze"] = np.where(df["bb_width"] < df["bb_width"].rolling(50).mean(), 1.0, 0.0)

    # MACD-like: difference between two EMAs
    ema_12 = close.ewm(span=12, adjust=False).mean()
    ema_26 = close.ewm(span=26, adjust=False).mean()
    macd_line = ema_12 - ema_26
    macd_signal = macd_line.ewm(span=9, adjust=False).mean()
    df["macd_line"] = macd_line / (close + 1e-10)
    df["macd_signal"] = macd_signal / (close + 1e-10)
    df["macd_hist"] = (macd_line - macd_signal) / (close + 1e-10)
    df["macd_cross"] = np.sign(macd_line - macd_signal)

    # Stochastic oscillator (14-period)
    lowest_low = low.rolling(14).min()
    highest_high = high.rolling(14).max()
    stoch_k = 100 * (close - lowest_low) / (highest_high - lowest_low + 1e-10)
    stoch_d = stoch_k.rolling(3).mean()
    df["stoch_k"] = stoch_k
    df["stoch_d"] = stoch_d
    df["stoch_diff"] = stoch_k - stoch_d

    # Volume of price movement (candle body and shadows)
    df["body"] = (close - open_).abs() / (atr_14 + 1e-10)
    df["upper_shadow"] = (high - pd.concat([close, open_], axis=1).max(axis=1)) / (atr_14 + 1e-10)
    df["lower_shadow"] = (pd.concat([close, open_], axis=1).min(axis=1) - low) / (atr_14 + 1e-10)
    df["candle_dir"] = np.sign(close - open_)

    # Rolling volatility (realised vol over 20 bars)
    log_ret = np.log(close / close.shift(1))
    df["realvol_20"] = log_ret.rolling(20).std()

    # Close relative to recent high/low channel (20-bar)
    roll_high_20 = high.rolling(20).max()
    roll_low_20 = low.rolling(20).min()
    df["chan_pct_20"] = (close - roll_low_20) / (roll_high_20 - roll_low_20 + 1e-10)

    # Lagged RSI and EMA cross for temporal context
    df["rsi_14_lag1"] = rsi_14.shift(1)
    df["rsi_14_lag2"] = rsi_14.shift(2)
    df["ema_cross_lag1"] = df["ema_cross"].shift(1)
    df["ema_cross_lag2"] = df["ema_cross"].shift(2)
    df["macd_hist_lag1"] = df["macd_hist"].shift(1)

    # RSI momentum: change in RSI
    df["rsi_delta_1"] = rsi_14.diff(1)
    df["rsi_delta_4"] = rsi_14.diff(4)

    # EMA9 slope (normalised)
    df["ema9_slope"] = ema_9.diff(3) / (ema_9.shift(3) + 1e-10)
    df["ema21_slope"] = ema_21.diff(3) / (ema_21.shift(3) + 1e-10)

    # Interaction: RSI * EMA cross direction
    df["rsi_ema_cross_interact"] = df["rsi_norm"] * df["ema_cross_signal"]

    # Fill NaN from indicator warm-up
    df = df.bfill().ffill()

    return df

# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title": "EMA Cross 9/21 + RSI14 Gradient Boost Scalper",
        "model_type": "GradientBoostingClassifier",
        "model_params": {
            "n_estimators": 400,
            "max_depth": 4,
            "learning_rate": 0.04,
            "subsample": 0.8,
            "min_samples_leaf": 20,
            "min_samples_split": 40,
            "max_features": "sqrt",
            "n_iter_no_change": 30,
            "validation_fraction": 0.1,
            "tol": 1e-4,
        },
        "signal_threshold": 0.56,
        "direction": "both",
        "stop_loss": 0.005,
        "take_profit": 0.010,
        "cooldown": 0,
        "max_positions": 1,
        "on_opposite": "reverse",
        "session_filter": [7, 17],
        "min_atr": 0.0002,
        "trend_filter": "sma_50",
        "target_horizon": 4,
        "objective": (
            "Maximize risk-adjusted return (Sharpe/Calmar) using a GradientBoostingClassifier "
            "with EMA 9/21 crossover as the primary signal source and RSI 14 as confirmation. "
            "Deep feature set includes MACD, Bollinger Bands, Stochastic, ATR normalisation, "
            "candle structure, lagged features and RSI/EMA interaction terms. "
            "Gradient boosting chosen for its ability to capture non-linear interactions between "
            "trend, momentum and volatility features without overfitting when regularised via "
            "subsample, max_features, and early stopping. Threshold 0.56 filters marginal signals. "
            "Session filter [7,17] focuses on London/NY overlap for highest EUR/USD liquidity. "
            "SL 0.5% / TP 1.0% gives 1:2 risk-reward aligned with scalper momentum targets. "
            "Reverse on opposite signal to stay in sync with fast EMA crossover momentum."
        ),
        "notes": (
            "EMA 9/21 cross captures short-term momentum shifts typical of active EUR/USD sessions. "
            "RSI 14 filters entries in extreme overbought/oversold conditions. "
            "NATR min_atr filter removes flat/low-vol periods. "
            "Trend filter (SMA 50) ensures longs only above and shorts only below the medium-term trend. "
            "n_iter_no_change=30 provides early stopping to prevent overfitting on the training split. "
            "400 estimators with depth 4 and lr 0.04 balance bias-variance tradeoff for intraday data."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 02:57:20
# Model            : Gradient Boosting
# Feature Eng.     : BB (20,2.0), ATR 14 + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# Bollinger Bands Squeeze Breakout Strategy — USD/JPY 15-min
# ============================================================

# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDJPY_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # ── Bollinger Bands (20, 2) ──────────────────────────────────────────────
    bb_period = 20
    bb_std    = 2.0

    bb_mid   = close.rolling(bb_period).mean()
    bb_sigma = close.rolling(bb_period).std(ddof=0)
    bb_upper = bb_mid + bb_std * bb_sigma
    bb_lower = bb_mid - bb_std * bb_sigma

    df["bb_mid"]   = bb_mid
    df["bb_upper"] = bb_upper
    df["bb_lower"] = bb_lower

    # Band width and %B — required features
    df["bb_width"] = (bb_upper - bb_lower) / bb_mid
    df["bb_pct"]   = (close - bb_lower) / (bb_upper - bb_lower)

    # ── ATR 14 & NATR ────────────────────────────────────────────────────────
    atr_period = 14
    prev_close = close.shift(1)
    tr = pd.concat([
        high - low,
        (high - prev_close).abs(),
        (low  - prev_close).abs()
    ], axis=1).max(axis=1)

    atr  = tr.ewm(span=atr_period, min_periods=atr_period, adjust=False).mean()
    natr = atr / close

    df["atr"]  = atr
    df["natr"] = natr

    # ── Squeeze detection ────────────────────────────────────────────────────
    # Keltner Channel (EMA20 ± 1.5 × ATR) for squeeze comparison
    kc_mid   = close.ewm(span=20, adjust=False).mean()
    kc_upper = kc_mid + 1.5 * atr
    kc_lower = kc_mid - 1.5 * atr

    df["squeeze"] = np.where(
        (bb_upper < kc_upper) & (bb_lower > kc_lower), 1.0, 0.0
    )

    # Rolling squeeze count (bars in squeeze over last 10 bars)
    df["squeeze_count"] = (
        df["squeeze"].rolling(10).sum()
    )

    # Band-width z-score (how compressed is the width vs recent history)
    bw_mean = df["bb_width"].rolling(50).mean()
    bw_std  = df["bb_width"].rolling(50).std(ddof=0)
    df["bb_width_zscore"] = (df["bb_width"] - bw_mean) / (bw_std + 1e-10)

    # ── Breakout momentum ────────────────────────────────────────────────────
    # Price distance from bands, normalised by ATR
    df["dist_upper"] = (close - bb_upper) / (atr + 1e-10)
    df["dist_lower"] = (close - bb_lower) / (atr + 1e-10)
    df["dist_mid"]   = (close - bb_mid)   / (atr + 1e-10)

    # ── Rate of change ────────────────────────────────────────────────────────
    for n in [1, 4, 8, 16]:
        df[f"roc_{n}"] = close.pct_change(n)

    # ── RSI 14 ───────────────────────────────────────────────────────────────
    delta  = close.diff()
    gain   = delta.clip(lower=0)
    loss   = (-delta).clip(lower=0)
    avg_g  = gain.ewm(span=14, min_periods=14, adjust=False).mean()
    avg_l  = loss.ewm(span=14, min_periods=14, adjust=False).mean()
    rs     = avg_g / (avg_l + 1e-10)
    df["rsi_14"] = 100.0 - 100.0 / (1.0 + rs)

    # RSI normalised to [-1, 1]
    df["rsi_norm"] = (df["rsi_14"] - 50.0) / 50.0

    # ── Momentum / trend context ──────────────────────────────────────────────
    ema_9  = close.ewm(span=9,  adjust=False).mean()
    ema_21 = close.ewm(span=21, adjust=False).mean()
    ema_50 = close.ewm(span=50, adjust=False).mean()

    df["ema_9_21_diff"] = (ema_9  - ema_21) / (atr + 1e-10)
    df["ema_21_50_diff"] = (ema_21 - ema_50) / (atr + 1e-10)

    # Price position relative to EMAs
    df["close_vs_ema9"]  = (close - ema_9)  / (atr + 1e-10)
    df["close_vs_ema50"] = (close - ema_50) / (atr + 1e-10)

    # ── Volume-proxy: ATR velocity ────────────────────────────────────────────
    df["atr_roc"]  = atr.pct_change(4)

    # ── MACD-style oscillator ─────────────────────────────────────────────────
    macd_line   = close.ewm(span=12, adjust=False).mean() - close.ewm(span=26, adjust=False).mean()
    macd_signal = macd_line.ewm(span=9, adjust=False).mean()
    df["macd_hist"] = (macd_line - macd_signal) / (atr + 1e-10)

    # ── Stochastic %K (14) ────────────────────────────────────────────────────
    low_14  = low.rolling(14).min()
    high_14 = high.rolling(14).max()
    df["stoch_k"] = (close - low_14) / (high_14 - low_14 + 1e-10)

    # ── Candle body / wick features ───────────────────────────────────────────
    body     = (close - open_).abs()
    candle_range = (high - low).replace(0, np.nan)
    df["body_ratio"]  = body / candle_range
    df["upper_wick"]  = (high - pd.concat([close, open_], axis=1).max(axis=1)) / (candle_range + 1e-10)
    df["lower_wick"]  = (pd.concat([close, open_], axis=1).min(axis=1) - low) / (candle_range + 1e-10)
    df["bull_candle"]  = np.where(close > open_, 1.0, 0.0)

    # ── Lagged bb_pct and bb_width ────────────────────────────────────────────
    for lag in [1, 2, 4]:
        df[f"bb_pct_lag{lag}"]   = df["bb_pct"].shift(lag)
        df[f"bb_width_lag{lag}"] = df["bb_width"].shift(lag)

    # ── Band width momentum (is it expanding?) ────────────────────────────────
    df["bb_width_chg1"] = df["bb_width"].diff(1)
    df["bb_width_chg4"] = df["bb_width"].diff(4)

    # ── Hour / session features ───────────────────────────────────────────────
    df["hour_sin"] = np.sin(2 * np.pi * df.index.hour / 24)
    df["hour_cos"] = np.cos(2 * np.pi * df.index.hour / 24)
    df["dow_sin"]  = np.sin(2 * np.pi * df.index.dayofweek / 5)
    df["dow_cos"]  = np.cos(2 * np.pi * df.index.dayofweek / 5)

    # ── Fill NaN from warm-up ─────────────────────────────────────────────────
    df = df.bfill().ffill()

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title": "USD/JPY BB Squeeze Breakout (GBM)",

        "model_type": "GradientBoostingClassifier",
        "model_params": {
            "n_estimators":     400,
            "max_depth":        4,
            "learning_rate":    0.04,
            "subsample":        0.75,
            "min_samples_leaf": 20,
            "max_features":     "sqrt",
            "validation_fraction": 0.1,
            "n_iter_no_change": 30,
            "tol":              1e-4,
            "random_state":     42,
        },

        "signal_threshold": 0.55,

        "direction":     "both",
        "stop_loss":     0.005,
        "take_profit":   0.010,
        "cooldown":      0,
        "max_positions": 1,
        "on_opposite":   "reverse",

        "session_filter": [6, 20],
        "min_atr":        0.0003,
        "trend_filter":   None,

        "target_horizon": 4,

        "objective": (
            "Maximize risk-adjusted return (Sharpe). "
            "GradientBoostingClassifier chosen for strong performance on tabular "
            "financial data with moderate feature counts. Deeper ensemble (400 "
            "estimators, depth 4) with early stopping captures non-linear BB "
            "squeeze patterns. Subsample=0.75 and sqrt features reduce overfitting. "
            "SL 0.5% / TP 1.0% gives 1:2 R:R ratio. Session filter 06-20 UTC covers "
            "London + NY sessions where USD/JPY liquidity is highest."
        ),
        "notes": (
            "Core signal: BB squeeze (narrow band width inside Keltner Channel) "
            "followed by band expansion. Features include band width z-score, "
            "breakout direction (dist_upper/lower), RSI, MACD histogram, "
            "stochastic %K, EMA spreads, candle structure, and lagged BB features. "
            "NATR used as min_atr filter to avoid low-volatility noise trades. "
            "Horizon=4 bars (1 hour on 15-min data) aligns with typical "
            "post-squeeze expansion duration."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 02:33:17
# Model            : Gradient Boosting
# Feature Eng.     : BB (20,2.0), RSI 14, Stochastic (14,3) + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/EURUSD_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):

    # ── Bollinger Bands (20, 2) ──────────────────────────────────────────────
    bb_period = 20
    bb_std    = 2.0
    bb_mid    = close.rolling(bb_period).mean()
    bb_std_v  = close.rolling(bb_period).std(ddof=0)
    bb_upper  = bb_mid + bb_std * bb_std_v
    bb_lower  = bb_mid - bb_std * bb_std_v

    df["bb_mid"]   = bb_mid
    df["bb_upper"] = bb_upper
    df["bb_lower"] = bb_lower
    df["bb_width"] = (bb_upper - bb_lower) / bb_mid
    df["bb_pct"]   = (close - bb_lower) / (bb_upper - bb_lower)

    # ── RSI 14 ───────────────────────────────────────────────────────────────
    rsi_period = 14
    delta      = close.diff()
    gain       = delta.clip(lower=0)
    loss       = -delta.clip(upper=0)
    avg_gain   = gain.ewm(com=rsi_period - 1, min_periods=rsi_period).mean()
    avg_loss   = loss.ewm(com=rsi_period - 1, min_periods=rsi_period).mean()
    rs         = avg_gain / avg_loss.replace(0, np.nan)
    df["rsi"]  = 100 - (100 / (1 + rs))

    # ── Stochastic Oscillator (K=14, D=3) ────────────────────────────────────
    stoch_k_period = 14
    stoch_d_period = 3
    lowest_low     = low.rolling(stoch_k_period).min()
    highest_high   = high.rolling(stoch_k_period).max()
    range_hl       = (highest_high - lowest_low).replace(0, np.nan)
    df["stoch_k"]  = 100 * (close - lowest_low) / range_hl
    df["stoch_d"]  = df["stoch_k"].rolling(stoch_d_period).mean()
    df["stoch_kd_diff"] = df["stoch_k"] - df["stoch_d"]

    # ── ATR (14) ──────────────────────────────────────────────────────────────
    atr_period = 14
    prev_close = close.shift(1)
    tr         = pd.concat([
        high - low,
        (high - prev_close).abs(),
        (low  - prev_close).abs()
    ], axis=1).max(axis=1)
    df["atr"]  = tr.ewm(com=atr_period - 1, min_periods=atr_period).mean()
    df["natr"] = df["atr"] / close

    # ── SMA filters ──────────────────────────────────────────────────────────
    df["sma_20"]  = close.rolling(20).mean()
    df["sma_50"]  = close.rolling(50).mean()
    df["sma_200"] = close.rolling(200).mean()
    df["price_vs_sma50"]  = close / df["sma_50"]  - 1
    df["price_vs_sma200"] = close / df["sma_200"] - 1

    # ── EMA cross ────────────────────────────────────────────────────────────
    ema_fast = close.ewm(span=8,  adjust=False).mean()
    ema_slow = close.ewm(span=21, adjust=False).mean()
    df["ema_cross"] = ema_fast - ema_slow

    # ── MACD ─────────────────────────────────────────────────────────────────
    ema12           = close.ewm(span=12, adjust=False).mean()
    ema26           = close.ewm(span=26, adjust=False).mean()
    macd_line       = ema12 - ema26
    macd_signal     = macd_line.ewm(span=9, adjust=False).mean()
    df["macd"]      = macd_line
    df["macd_sig"]  = macd_signal
    df["macd_hist"] = macd_line - macd_signal

    # ── Momentum / Rate-of-change ────────────────────────────────────────────
    df["roc_4"]  = close.pct_change(4)
    df["roc_8"]  = close.pct_change(8)
    df["roc_16"] = close.pct_change(16)

    # ── Candle features ───────────────────────────────────────────────────────
    df["candle_body"]   = (close - open_) / close
    df["candle_range"]  = (high - low)    / close
    df["upper_shadow"]  = (high - pd.concat([close, open_], axis=1).max(axis=1)) / close
    df["lower_shadow"]  = (pd.concat([close, open_], axis=1).min(axis=1) - low)  / close

    # ── Volatility regime ─────────────────────────────────────────────────────
    df["vol_ratio"] = df["atr"] / df["atr"].rolling(50).mean()

    # ── RSI regime bins (np.where instead of pd.cut) ─────────────────────────
    df["rsi_oversold"]  = np.where(df["rsi"] < 30, 1, 0)
    df["rsi_overbought"]= np.where(df["rsi"] > 70, 1, 0)
    df["rsi_mid"]       = np.where((df["rsi"] >= 40) & (df["rsi"] <= 60), 1, 0)

    # ── Stochastic regime bins ────────────────────────────────────────────────
    df["stoch_oversold"]   = np.where(df["stoch_k"] < 20, 1, 0)
    df["stoch_overbought"] = np.where(df["stoch_k"] > 80, 1, 0)

    # ── BB regime bins ────────────────────────────────────────────────────────
    df["bb_squeeze"]   = np.where(df["bb_width"] < df["bb_width"].rolling(50).quantile(0.20), 1, 0)
    df["bb_expansion"] = np.where(df["bb_width"] > df["bb_width"].rolling(50).quantile(0.80), 1, 0)
    df["price_below_bb_lower"] = np.where(close < bb_lower, 1, 0)
    df["price_above_bb_upper"] = np.where(close > bb_upper, 1, 0)

    # ── Volume proxy — bar range z-score ──────────────────────────────────────
    range_series        = high - low
    range_mean          = range_series.rolling(20).mean()
    range_std           = range_series.rolling(20).std(ddof=0)
    df["range_zscore"]  = (range_series - range_mean) / range_std.replace(0, np.nan)

    # ── Lagged features ───────────────────────────────────────────────────────
    for lag in [1, 2, 3, 4]:
        df[f"rsi_lag{lag}"]     = df["rsi"].shift(lag)
        df[f"stoch_k_lag{lag}"] = df["stoch_k"].shift(lag)
        df[f"bb_pct_lag{lag}"]  = df["bb_pct"].shift(lag)
        df[f"macd_hist_lag{lag}"] = df["macd_hist"].shift(lag)

    # ── Interaction features ──────────────────────────────────────────────────
    df["rsi_x_bb_pct"]       = df["rsi"] * df["bb_pct"]
    df["stoch_x_bb_pct"]     = df["stoch_k"] * df["bb_pct"]
    df["macd_x_ema_cross"]   = df["macd_hist"] * df["ema_cross"]
    df["rsi_x_stoch_kd"]     = df["rsi"] * df["stoch_kd_diff"]

    # ── Fill NaN from warm-up ─────────────────────────────────────────────────
    df = df.bfill().ffill()

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title": "EUR/USD Stoch+BB+RSI Gradient Boosting Mean-Rev",
        "model_type": "GradientBoostingClassifier",
        "model_params": {
            "n_estimators":     400,
            "max_depth":        4,
            "learning_rate":    0.04,
            "subsample":        0.75,
            "min_samples_leaf": 20,
            "max_features":     "sqrt",
            "validation_fraction": 0.1,
            "n_iter_no_change": 30,
            "tol":              1e-4,
            "random_state":     42,
        },
        "signal_threshold": 0.56,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [6, 18],
        "min_atr":          0.0002,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective": (
            "Maximize risk-adjusted return (Sharpe/Calmar) on EUR/USD 15-min data. "
            "GradientBoostingClassifier chosen for strong out-of-bag regularisation "
            "via subsample=0.75 and early stopping (n_iter_no_change=30). "
            "max_depth=4 limits overfitting on mean-reversion regime. "
            "learning_rate=0.04 with 400 trees balances bias-variance. "
            "Signal threshold 0.56 filters low-confidence signals for better precision. "
            "Session filter 06-18 UTC targets London+NY overlap with highest liquidity. "
            "SL=0.5%, TP=1.0% gives 1:2 R:R aligned with mean-reversion edge. "
            "Target horizon=4 bars (1 hour) captures short-term mean-reversion cycles."
        ),
        "notes": (
            "Features: Stochastic(14,3), BB(20,2), RSI(14) as primary signals. "
            "Supplemented by MACD, EMA cross, ATR volatility filter, candle body/shadow, "
            "range z-score, lagged versions of key oscillators, and interaction terms. "
            "Regime bins (oversold/overbought/squeeze/expansion) add non-linear context. "
            "min_atr=0.0002 avoids trading during dead/illiquid periods."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-05 10:27:10
# Model            : XGBoost
# Feature Eng.     : EMA (50,200), ATR 14 + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/EURUSD_15min.parquet"
START_DATE = "2025-04-23"
END_DATE   = "2026-04-23"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # ── EMA 50 and EMA 200 ──────────────────────────────────────────────────
    ema_50  = close.ewm(span=50,  adjust=False).mean()
    ema_200 = close.ewm(span=200, adjust=False).mean()

    df["ema_50"]    = ema_50
    df["ema_200"]   = ema_200
    df["dm_ema_50"]  = (close - ema_50)  / ema_50
    df["dm_ema_200"] = (close - ema_200) / ema_200

    # EMA cross signal: positive when fast > slow
    df["ema_cross"]      = ema_50 - ema_200
    df["ema_cross_norm"] = df["ema_cross"] / ema_200

    # Cross direction change (momentum of the spread)
    df["ema_cross_delta"] = df["ema_cross"].diff(1)
    df["ema_cross_accel"] = df["ema_cross_delta"].diff(1)

    # ── ATR 14 ──────────────────────────────────────────────────────────────
    prev_close = close.shift(1)
    tr = pd.concat([
        high - low,
        (high - prev_close).abs(),
        (low  - prev_close).abs()
    ], axis=1).max(axis=1)

    atr = tr.ewm(span=14, adjust=False).mean()
    df["atr"]  = atr
    df["natr"] = atr / close

    # ── Price momentum features ─────────────────────────────────────────────
    for lag in [1, 2, 4, 8, 16]:
        df[f"ret_{lag}"] = close.pct_change(lag)

    # ── Volatility regime ───────────────────────────────────────────────────
    df["atr_ratio"]    = atr / atr.rolling(50).mean()          # ATR vs its own MA
    df["natr_ma20"]    = df["natr"].rolling(20).mean()

    # ── RSI 14 ──────────────────────────────────────────────────────────────
    delta  = close.diff(1)
    gain   = delta.clip(lower=0)
    loss   = (-delta).clip(lower=0)
    avg_g  = gain.ewm(span=14, adjust=False).mean()
    avg_l  = loss.ewm(span=14, adjust=False).mean()
    rs     = avg_g / avg_l.replace(0, np.nan)
    df["rsi_14"] = 100 - (100 / (1 + rs))
    df["rsi_delta"] = df["rsi_14"].diff(1)

    # ── MACD (12/26/9) ──────────────────────────────────────────────────────
    ema_12   = close.ewm(span=12, adjust=False).mean()
    ema_26   = close.ewm(span=26, adjust=False).mean()
    macd     = ema_12 - ema_26
    signal   = macd.ewm(span=9, adjust=False).mean()
    df["macd"]          = macd
    df["macd_signal"]   = signal
    df["macd_hist"]     = macd - signal
    df["macd_hist_delta"] = df["macd_hist"].diff(1)

    # ── Bollinger Bands (20, 2σ) ─────────────────────────────────────────────
    bb_mid  = close.rolling(20).mean()
    bb_std  = close.rolling(20).std()
    bb_up   = bb_mid + 2 * bb_std
    bb_lo   = bb_mid - 2 * bb_std
    df["bb_pos"]   = (close - bb_lo) / (bb_up - bb_lo).replace(0, np.nan)
    df["bb_width"] = (bb_up - bb_lo) / bb_mid

    # ── Stochastic %K / %D (14, 3) ──────────────────────────────────────────
    low14  = low.rolling(14).min()
    high14 = high.rolling(14).max()
    stoch_k = 100 * (close - low14) / (high14 - low14).replace(0, np.nan)
    stoch_d = stoch_k.rolling(3).mean()
    df["stoch_k"] = stoch_k
    df["stoch_d"] = stoch_d
    df["stoch_kd"] = stoch_k - stoch_d

    # ── Volume / body / wick features ───────────────────────────────────────
    body    = (close - open_).abs()
    candle  = (high - low).replace(0, np.nan)
    df["body_ratio"]  = body / candle
    df["upper_wick"]  = (high - pd.concat([close, open_], axis=1).max(axis=1)) / candle
    df["lower_wick"]  = (pd.concat([close, open_], axis=1).min(axis=1) - low) / candle
    df["bull_candle"]  = np.where(close > open_, 1, 0)

    # ── Rolling z-score of close vs SMA 50 ──────────────────────────────────
    sma_50       = close.rolling(50).mean()
    sma_50_std   = close.rolling(50).std()
    df["zscore_50"] = (close - sma_50) / sma_50_std.replace(0, np.nan)

    # ── High/Low breakout flags ──────────────────────────────────────────────
    df["high_20_break"] = np.where(close > high.rolling(20).max().shift(1), 1, 0)
    df["low_20_break"]  = np.where(close < low.rolling(20).min().shift(1),  1, 0)

    # ── Time-of-day features (cyclical encoding) ─────────────────────────────
    if hasattr(df.index, 'hour'):
        hour = df.index.hour + df.index.minute / 60.0
        df["hour_sin"] = np.sin(2 * np.pi * hour / 24.0)
        df["hour_cos"] = np.cos(2 * np.pi * hour / 24.0)

    # ── Fill NaN from indicator warm-up ─────────────────────────────────────
    df = df.bfill().ffill()

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "EMA Cross 50/200 + ATR Momentum (XGBoost)",
        "model_type":       "XGBClassifier",
        "model_params": {
            "n_estimators":       400,
            "max_depth":          4,
            "learning_rate":      0.04,
            "subsample":          0.75,
            "colsample_bytree":   0.70,
            "min_child_weight":   3,
            "gamma":              0.15,
            "reg_alpha":          0.10,
            "reg_lambda":         1.50,
            "objective":          "binary:logistic",
            "random_state":       42,
            "n_jobs":             -1,
        },
        "signal_threshold": 0.55,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [6, 20],
        "min_atr":          0.0002,
        "trend_filter":     "sma_50",
        "target_horizon":   4,
        "objective": (
            "Maximize risk-adjusted return (Sharpe/Calmar) on EUR/USD 15-min data. "
            "EMA 50/200 cross provides the primary trend regime filter. "
            "ATR 14 gates entries by volatility (min_atr avoids dead-market noise). "
            "XGBoost chosen for its ability to capture non-linear feature interactions. "
            "Conservative depth=4 and regularisation (alpha/lambda) prevent overfitting "
            "on the relatively short 1-year window. 2:1 reward/risk (SL=0.5%, TP=1.0%) "
            "ensures positive expectancy even at modest hit-rates. Session filter 06-20 UTC "
            "keeps the strategy in liquid London/NY hours only."
        ),
        "notes": (
            "Feature set combines trend (EMA cross, z-score), momentum (RSI, MACD, returns), "
            "volatility (ATR ratio, BB width), and price structure (body/wick ratios, "
            "stochastic). Cyclical hour encoding captures intraday seasonality without "
            "introducing lookahead. bfill().ffill() handles EMA warm-up NaNs gracefully."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 02:35:43
# Model            : Gradient Boosting
# Feature Eng.     : SMA (20,50,200) + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/EURUSD_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # ── SMA core features (required) ──────────────────────────────────────
    for period in [20, 50, 200]:
        sma = close.rolling(period).mean()
        df[f"sma_{period}"] = sma
        df[f"dm_sma_{period}"] = (close - sma) / sma

    # ── SMA slope (momentum of the moving average itself) ──────────────────
    for period in [20, 50, 200]:
        df[f"sma_{period}_slope"] = df[f"sma_{period}"].diff(5) / df[f"sma_{period}"].shift(5)

    # ── SMA crossover signals ──────────────────────────────────────────────
    df["sma_20_50_cross"]  = df["sma_20"] - df["sma_50"]
    df["sma_50_200_cross"] = df["sma_50"] - df["sma_200"]
    df["sma_20_200_cross"] = df["sma_20"] - df["sma_200"]

    # Sign of crossover difference (trend direction)
    df["trend_20_50"]  = np.where(df["sma_20_50_cross"]  > 0, 1, -1)
    df["trend_50_200"] = np.where(df["sma_50_200_cross"] > 0, 1, -1)

    # ── Price momentum ────────────────────────────────────────────────────
    for lag in [1, 4, 8, 16, 32]:
        df[f"return_{lag}"] = close.pct_change(lag)

    # ── Volatility features ───────────────────────────────────────────────
    # True Range
    prev_close = close.shift(1)
    tr = pd.concat([
        high - low,
        (high - prev_close).abs(),
        (low  - prev_close).abs()
    ], axis=1).max(axis=1)

    for atr_period in [14, 50]:
        atr = tr.rolling(atr_period).mean()
        df[f"atr_{atr_period}"] = atr
        df[f"natr_{atr_period}"] = atr / close

    # Rolling realised volatility
    log_ret = np.log(close / close.shift(1))
    for vol_period in [20, 50]:
        df[f"realvol_{vol_period}"] = log_ret.rolling(vol_period).std()

    # ── Bollinger Bands (20, 2σ) ──────────────────────────────────────────
    bb_mid = close.rolling(20).mean()
    bb_std = close.rolling(20).std()
    bb_upper = bb_mid + 2 * bb_std
    bb_lower = bb_mid - 2 * bb_std
    df["bb_width"]    = (bb_upper - bb_lower) / bb_mid
    df["bb_pct"]      = (close - bb_lower) / (bb_upper - bb_lower + 1e-12)
    df["bb_position"] = (close - bb_mid) / (bb_std + 1e-12)

    # ── RSI (14) ──────────────────────────────────────────────────────────
    delta = close.diff()
    gain  = delta.clip(lower=0).rolling(14).mean()
    loss  = (-delta.clip(upper=0)).rolling(14).mean()
    rs    = gain / (loss + 1e-12)
    df["rsi_14"] = 100 - (100 / (1 + rs))

    # RSI normalised to [-1, 1]
    df["rsi_14_norm"] = (df["rsi_14"] - 50) / 50

    # ── MACD (12, 26, 9) ─────────────────────────────────────────────────
    ema12 = close.ewm(span=12, adjust=False).mean()
    ema26 = close.ewm(span=26, adjust=False).mean()
    macd_line   = ema12 - ema26
    signal_line = macd_line.ewm(span=9, adjust=False).mean()
    df["macd"]          = macd_line
    df["macd_signal"]   = signal_line
    df["macd_hist"]     = macd_line - signal_line
    df["macd_hist_chg"] = df["macd_hist"].diff()

    # Normalise MACD by price
    df["macd_norm"]   = df["macd"]      / close
    df["macd_hist_norm"] = df["macd_hist"] / close

    # ── Stochastic Oscillator (14, 3) ─────────────────────────────────────
    low14  = low.rolling(14).min()
    high14 = high.rolling(14).max()
    stoch_k = 100 * (close - low14) / (high14 - low14 + 1e-12)
    stoch_d = stoch_k.rolling(3).mean()
    df["stoch_k"]    = stoch_k
    df["stoch_d"]    = stoch_d
    df["stoch_diff"] = stoch_k - stoch_d

    # ── Rate-of-change ────────────────────────────────────────────────────
    for roc_period in [5, 10, 20]:
        df[f"roc_{roc_period}"] = (close - close.shift(roc_period)) / (close.shift(roc_period) + 1e-12)

    # ── Candle body and wick features ─────────────────────────────────────
    body      = (close - open_).abs()
    candle_rng = (high - low).replace(0, np.nan)
    df["body_ratio"]       = body / candle_rng
    df["upper_wick_ratio"] = (high - pd.concat([close, open_], axis=1).max(axis=1)) / candle_rng
    df["lower_wick_ratio"] = (pd.concat([close, open_], axis=1).min(axis=1) - low)  / candle_rng
    df["candle_direction"]  = np.where(close >= open_, 1, -1)

    # ── Time-of-day features (hour) ───────────────────────────────────────
    hour = df.index.hour
    df["hour_sin"] = np.sin(2 * np.pi * hour / 24)
    df["hour_cos"] = np.cos(2 * np.pi * hour / 24)

    # ── Lagged returns as features ────────────────────────────────────────
    for lag in [1, 2, 3, 4]:
        df[f"close_lag_{lag}"] = close.shift(lag)
        df[f"ret_lag_{lag}"]   = log_ret.shift(lag)

    # ── Volume proxy: range-based ─────────────────────────────────────────
    df["range_abs"]  = high - low
    df["range_norm"] = (high - low) / close

    # ── High-Low channel position ─────────────────────────────────────────
    for ch_period in [20, 50]:
        ch_high = high.rolling(ch_period).max()
        ch_low  = low.rolling(ch_period).min()
        df[f"channel_pos_{ch_period}"] = (close - ch_low) / (ch_high - ch_low + 1e-12)

    # ── Fill NaN from warm-up ─────────────────────────────────────────────
    df = df.bfill().ffill()

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "EUR/USD SMA Trend + Multi-Indicator GBM Scalper",
        "model_type":       "GradientBoostingClassifier",
        "model_params":     {
            "n_estimators":     400,
            "max_depth":        4,
            "learning_rate":    0.04,
            "subsample":        0.75,
            "max_features":     "sqrt",
            "min_samples_leaf": 20,
            "min_samples_split": 40,
            "validation_fraction": 0.1,
            "n_iter_no_change": 30,
            "tol":              1e-4,
            "random_state":     42,
        },
        "signal_threshold": 0.55,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [6, 18],
        "min_atr":          None,
        "trend_filter":     "sma_50",
        "target_horizon":   4,
        "objective": (
            "Maximise risk-adjusted return (Sharpe / Calmar). "
            "GradientBoostingClassifier chosen for its strong performance on "
            "tabular financial data with moderate feature sets. "
            "Shallow trees (max_depth=4) with high n_estimators and a low "
            "learning_rate reduce overfitting. subsample=0.75 adds stochastic "
            "regularisation. Early stopping via n_iter_no_change prevents "
            "over-training on the validation split. "
            "SL=0.5%, TP=1.0% gives a 1:2 risk-reward ratio, "
            "targeting positive expectancy even at sub-60% accuracy. "
            "Session filter (06-18 UTC) restricts trading to liquid hours "
            "covering London and New York overlap for EUR/USD. "
            "SMA-50 trend filter ensures trades align with the medium-term "
            "trend, reducing counter-trend noise."
        ),
        "notes": (
            "Features include required SMA(20,50,200) distances and crossovers, "
            "RSI-14, MACD histogram, Bollinger Band position, Stochastic, ATR, "
            "realised volatility, rate-of-change, candle structure ratios, "
            "channel position, lagged returns, and cyclical time encoding. "
            "Target horizon of 4 bars (1 hour) on 15-min data balances "
            "signal frequency with meaningful directional moves."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 02:01:39
# Model            : Gradient Boosting
# Feature Eng.     : BB (20,2.0), ATR 14 + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDJPY_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # ── Bollinger Bands (period=20, std_dev=2.0) ──────────────────────────────
    bb_period = 20
    bb_std    = 2.0

    bb_mid   = close.rolling(bb_period).mean()
    bb_sigma = close.rolling(bb_period).std(ddof=0)
    bb_upper = bb_mid + bb_std * bb_sigma
    bb_lower = bb_mid - bb_std * bb_sigma

    df["bb_mid"]   = bb_mid
    df["bb_upper"] = bb_upper
    df["bb_lower"] = bb_lower

    # bb_width: normalised band width (volatility proxy)
    df["bb_width"] = (bb_upper - bb_lower) / bb_mid

    # bb_pct: position of close within the band [0, 1]
    band_range = bb_upper - bb_lower
    df["bb_pct"] = (close - bb_lower) / band_range

    # Distance from close to mid in units of band width
    df["bb_dist_mid"] = (close - bb_mid) / bb_mid

    # ── ATR (period=14) ───────────────────────────────────────────────────────
    atr_period = 14
    prev_close = close.shift(1)
    tr = pd.concat([
        high - low,
        (high - prev_close).abs(),
        (low  - prev_close).abs()
    ], axis=1).max(axis=1)

    atr  = tr.ewm(span=atr_period, min_periods=atr_period, adjust=False).mean()
    natr = atr / close

    df["atr"]  = atr
    df["natr"] = natr

    # ── Momentum / trend features ─────────────────────────────────────────────
    # Rate of change at multiple horizons
    for n in [1, 4, 8, 16]:
        df[f"roc_{n}"] = close.pct_change(n)

    # RSI (14)
    rsi_period = 14
    delta  = close.diff()
    gain   = delta.clip(lower=0)
    loss   = (-delta).clip(lower=0)
    avg_gain = gain.ewm(span=rsi_period, min_periods=rsi_period, adjust=False).mean()
    avg_loss = loss.ewm(span=rsi_period, min_periods=rsi_period, adjust=False).mean()
    rs  = avg_gain / (avg_loss + 1e-10)
    rsi = 100 - (100 / (1 + rs))
    df["rsi_14"] = rsi

    # RSI derived: distance from 50 (centred, normalised)
    df["rsi_dev"] = (rsi - 50) / 50

    # ── MACD (12, 26, 9) ──────────────────────────────────────────────────────
    ema12 = close.ewm(span=12, adjust=False).mean()
    ema26 = close.ewm(span=26, adjust=False).mean()
    macd_line   = ema12 - ema26
    macd_signal = macd_line.ewm(span=9, adjust=False).mean()
    macd_hist   = macd_line - macd_signal

    df["macd_line"]   = macd_line
    df["macd_signal"] = macd_signal
    df["macd_hist"]   = macd_hist

    # ── Trend (SMA 50) ────────────────────────────────────────────────────────
    sma50 = close.rolling(50).mean()
    df["sma_50"]        = sma50
    df["close_vs_sma50"] = (close - sma50) / sma50   # normalised distance

    # ── Volume / candle structure features ────────────────────────────────────
    body      = (close - open_).abs()
    candle_rng = high - low
    df["body_ratio"]  = body / (candle_rng + 1e-10)   # body as fraction of range
    df["upper_wick"]  = (high  - pd.concat([close, open_], axis=1).max(axis=1)) / (candle_rng + 1e-10)
    df["lower_wick"]  = (pd.concat([close, open_], axis=1).min(axis=1) - low)   / (candle_rng + 1e-10)
    df["candle_dir"]  = np.where(close > open_, 1.0, -1.0)  # bullish / bearish bar

    # ── Lagged bb_pct & rsi (to give the model recent history) ───────────────
    for lag in [1, 2, 3]:
        df[f"bb_pct_lag{lag}"] = df["bb_pct"].shift(lag)
        df[f"rsi_lag{lag}"]    = df["rsi_14"].shift(lag)
        df[f"macd_hist_lag{lag}"] = df["macd_hist"].shift(lag)

    # ── Volatility regime flag ────────────────────────────────────────────────
    natr_ma = natr.rolling(50).mean()
    df["vol_regime"] = np.where(natr > natr_ma, 1.0, 0.0)   # 1 = high-vol regime

    # ── BB squeeze detection ──────────────────────────────────────────────────
    bb_width_ma = df["bb_width"].rolling(50).mean()
    df["bb_squeeze"] = np.where(df["bb_width"] < bb_width_ma, 1.0, 0.0)

    # ── Mean-reversion signal strength ────────────────────────────────────────
    # Positive → oversold (close below lower band), Negative → overbought
    df["mr_signal"] = 0.5 - df["bb_pct"]   # centred: +0.5 at lower band, -0.5 at upper

    # ── Fill NaN from warm-up ─────────────────────────────────────────────────
    df = df.bfill().ffill()

    return df

# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "USD/JPY BB Mean-Reversion + ATR Gradient Boost",
        "model_type":       "GradientBoostingClassifier",
        "model_params":     {
            "n_estimators":      500,
            "max_depth":         4,
            "learning_rate":     0.03,
            "subsample":         0.8,
            "min_samples_leaf":  20,
            "max_features":      "sqrt",
            "validation_fraction": 0.1,
            "n_iter_no_change":  30,
            "tol":               1e-4,
            "random_state":      42,
        },
        "signal_threshold": 0.55,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   None,
        "min_atr":          None,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective": (
            "Maximise Sharpe ratio via a Gradient Boosting classifier trained on "
            "Bollinger Band position (bb_pct), normalised bandwidth (bb_width), "
            "ATR/NATR, RSI, MACD histogram, candle structure, and lagged features. "
            "GBM chosen for its ability to capture non-linear interactions between "
            "volatility (ATR) and mean-reversion (BB) signals. n_iter_no_change "
            "acts as early stopping to prevent overfitting on the 15-min USDJPY series. "
            "SL=0.5% / TP=1.0% gives a 1:2 risk-reward; threshold=0.55 reduces noise trades."
        ),
        "notes": (
            "Bollinger Bands are the primary mean-reversion anchor; ATR/NATR filter "
            "entries to adequate volatility bars. RSI and MACD provide momentum context "
            "to avoid fading strong trends. Lagged features (up to 3 bars) give the model "
            "short-term regime memory without look-ahead. vol_regime and bb_squeeze flags "
            "allow the model to differentiate trending vs. ranging conditions automatically. "
            "No session filter applied — USDJPY is liquid across Asian and European sessions."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 01:41:21
# Model            : Gradient Boosting
# Feature Eng.     : SMA (20,50,200), BB (20,2.0), RSI 14, MACD (12,26,9), ATR 14 + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/GBPUSD_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):

    # ── SMA 20, 50, 200 + distance from close ──────────────────────────────
    for period in [20, 50, 200]:
        sma = close.rolling(period).mean()
        df[f"sma_{period}"] = sma
        df[f"dm_sma_{period}"] = (close - sma) / sma

    # ── Bollinger Bands (20, 2) ─────────────────────────────────────────────
    bb_mid   = close.rolling(20).mean()
    bb_std   = close.rolling(20).std(ddof=0)
    bb_upper = bb_mid + 2.0 * bb_std
    bb_lower = bb_mid - 2.0 * bb_std
    df["bb_mid"]   = bb_mid
    df["bb_upper"] = bb_upper
    df["bb_lower"] = bb_lower
    df["bb_width"] = (bb_upper - bb_lower) / bb_mid
    bb_range       = bb_upper - bb_lower
    df["bb_pct"]   = np.where(bb_range != 0, (close - bb_lower) / bb_range, 0.5)

    # ── RSI 14 ─────────────────────────────────────────────────────────────
    delta     = close.diff()
    gain      = delta.clip(lower=0)
    loss      = (-delta).clip(lower=0)
    avg_gain  = gain.ewm(com=13, min_periods=14, adjust=False).mean()
    avg_loss  = loss.ewm(com=13, min_periods=14, adjust=False).mean()
    rs        = np.where(avg_loss != 0, avg_gain / avg_loss, 100.0)
    df["rsi_14"] = 100.0 - (100.0 / (1.0 + rs))

    # ── MACD (12, 26, 9) ───────────────────────────────────────────────────
    ema_12          = close.ewm(span=12, adjust=False).mean()
    ema_26          = close.ewm(span=26, adjust=False).mean()
    macd_line       = ema_12 - ema_26
    signal_line     = macd_line.ewm(span=9, adjust=False).mean()
    df["macd_line"] = macd_line
    df["macd_signal"] = signal_line
    df["macd_hist"] = macd_line - signal_line

    # ── ATR 14 + NATR ──────────────────────────────────────────────────────
    prev_close  = close.shift(1)
    tr          = pd.concat([
        high - low,
        (high - prev_close).abs(),
        (low  - prev_close).abs()
    ], axis=1).max(axis=1)
    atr         = tr.ewm(com=13, min_periods=14, adjust=False).mean()
    df["atr_14"] = atr
    df["natr"]   = np.where(close != 0, atr / close, 0.0)

    # ── Price momentum (returns over multiple horizons) ────────────────────
    for lag in [1, 4, 8, 16]:
        df[f"ret_{lag}"] = close.pct_change(lag)

    # ── Candle body & wick features ────────────────────────────────────────
    body             = (close - open_).abs()
    candle_range     = (high - low).replace(0, np.nan)
    df["body_ratio"] = body / candle_range
    df["upper_wick"] = np.where(
        candle_range.notna(),
        (high - close.combine(open_, max)) / candle_range,
        0.0
    )
    df["lower_wick"] = np.where(
        candle_range.notna(),
        (close.combine(open_, min) - low) / candle_range,
        0.0
    )
    df["candle_dir"] = np.where(close >= open_, 1.0, -1.0)

    # ── Volume proxy: normalised candle range ──────────────────────────────
    rolling_range    = candle_range.rolling(20).mean()
    df["norm_range"] = np.where(
        rolling_range != 0,
        (high - low) / rolling_range,
        1.0
    )

    # ── RSI derived features ───────────────────────────────────────────────
    df["rsi_ob"]    = np.where(df["rsi_14"] > 70, 1.0, 0.0)
    df["rsi_os"]    = np.where(df["rsi_14"] < 30, 1.0, 0.0)
    df["rsi_mid"]   = df["rsi_14"] - 50.0
    df["rsi_slope"] = df["rsi_14"].diff(3)

    # ── MACD histogram slope ───────────────────────────────────────────────
    df["macd_hist_slope"] = df["macd_hist"].diff(2)
    df["macd_cross"]      = np.where(
        (df["macd_hist"] > 0) & (df["macd_hist"].shift(1) <= 0), 1.0,
        np.where(
            (df["macd_hist"] < 0) & (df["macd_hist"].shift(1) >= 0), -1.0,
            0.0
        )
    )

    # ── Bollinger squeeze (low volatility precursor) ───────────────────────
    bb_width_ma         = df["bb_width"].rolling(20).mean()
    df["bb_squeeze"]    = np.where(df["bb_width"] < bb_width_ma, 1.0, 0.0)

    # ── SMA slope features ─────────────────────────────────────────────────
    df["sma_20_slope"] = df["sma_20"].pct_change(4)
    df["sma_50_slope"] = df["sma_50"].pct_change(8)

    # ── Cross-SMA alignment (trend structure) ──────────────────────────────
    df["sma20_above_50"]  = np.where(df["sma_20"] > df["sma_50"],  1.0, 0.0)
    df["sma50_above_200"] = np.where(df["sma_50"] > df["sma_200"], 1.0, 0.0)
    df["close_above_200"] = np.where(close > df["sma_200"],        1.0, 0.0)

    # ── Lagged close returns as additional features ────────────────────────
    for lag in [1, 2, 3]:
        df[f"close_lag_{lag}"] = close.shift(lag)

    # ── Rolling volatility (std of returns) ────────────────────────────────
    df["vol_10"]  = close.pct_change().rolling(10).std()
    df["vol_20"]  = close.pct_change().rolling(20).std()
    df["vol_ratio"] = np.where(
        df["vol_20"] != 0,
        df["vol_10"] / df["vol_20"],
        1.0
    )

    # ── Hour-of-day (London/NY session proxy) ──────────────────────────────
    if hasattr(df.index, "hour"):
        df["hour_sin"] = np.sin(2 * np.pi * df.index.hour / 24.0)
        df["hour_cos"] = np.cos(2 * np.pi * df.index.hour / 24.0)
    else:
        df["hour_sin"] = 0.0
        df["hour_cos"] = 1.0

    # ── Fill NaN from warm-up periods ──────────────────────────────────────
    df = df.bfill().ffill()

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "GBP/USD Gradient Boosting Trend + Mean-Reversion",
        "model_type":       "GradientBoostingClassifier",
        "model_params":     {
            "n_estimators":     400,
            "max_depth":        4,
            "learning_rate":    0.04,
            "subsample":        0.8,
            "min_samples_leaf": 20,
            "max_features":     "sqrt",
            "validation_fraction": 0.1,
            "n_iter_no_change": 30,
            "tol":              1e-4,
            "random_state":     42,
        },
        "signal_threshold": 0.55,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [6, 20],
        "min_atr":          0.0002,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective": (
            "Maximize risk-adjusted return (Sharpe/Calmar) on GBP/USD 15-min. "
            "GradientBoostingClassifier with 400 shallow trees (depth 4) and a "
            "conservative learning rate of 0.04 avoids overfitting while capturing "
            "non-linear interactions between trend (SMA alignment, slope), momentum "
            "(MACD histogram, RSI), and volatility (ATR, BB squeeze) features. "
            "SL=0.5% / TP=1.0% gives 1:2 R/R. Session filter 06-20 UTC covers "
            "London open through NY close where GBP/USD liquidity is highest. "
            "min_atr filter avoids flat/illiquid bars."
        ),
        "notes": (
            "Features include multi-period SMA distances, Bollinger Band pct/width, "
            "RSI with overbought/oversold flags, MACD histogram slope and crossover, "
            "ATR-normalised volatility, candle body/wick ratios, rolling vol ratio, "
            "and hour-of-day cyclical encoding. target_horizon=4 (1-hour forward) "
            "balances signal frequency against predictability at 15-min resolution."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 01:35:33
# Model            : Gradient Boosting
# Feature Eng.     : RSI 14, MACD (12,26,9) + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/NZDUSD_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):

    # ── RSI 14 ──────────────────────────────────────────────────────────────
    delta = close.diff()
    gain  = delta.clip(lower=0)
    loss  = (-delta).clip(lower=0)
    avg_gain = gain.ewm(alpha=1/14, min_periods=14, adjust=False).mean()
    avg_loss = loss.ewm(alpha=1/14, min_periods=14, adjust=False).mean()
    rs = avg_gain / avg_loss.replace(0, np.nan)
    df["rsi_14"] = 100 - (100 / (1 + rs))

    # RSI derived signals
    df["rsi_ob"]      = np.where(df["rsi_14"] > 70, 1, 0)   # overbought flag
    df["rsi_os"]      = np.where(df["rsi_14"] < 30, 1, 0)   # oversold flag
    df["rsi_mid"]     = df["rsi_14"] - 50                    # centred
    df["rsi_slope"]   = df["rsi_14"].diff(3)                 # momentum of RSI
    df["rsi_accel"]   = df["rsi_slope"].diff(2)              # acceleration

    # RSI regime: above/below 50
    df["rsi_bull"]    = np.where(df["rsi_14"] > 50, 1, -1)

    # ── MACD (12, 26, 9) ────────────────────────────────────────────────────
    ema12 = close.ewm(span=12, adjust=False).mean()
    ema26 = close.ewm(span=26, adjust=False).mean()
    macd_line   = ema12 - ema26
    signal_line = macd_line.ewm(span=9, adjust=False).mean()
    macd_hist   = macd_line - signal_line

    df["macd_line"]   = macd_line
    df["macd_signal"] = signal_line
    df["macd_hist"]   = macd_hist

    # MACD derived
    df["macd_cross"]     = np.where(macd_line > signal_line, 1, -1)
    df["macd_hist_sign"] = np.where(macd_hist > 0, 1, -1)
    df["macd_hist_chg"]  = macd_hist.diff(1)          # histogram change
    df["macd_hist_accel"]= df["macd_hist_chg"].diff(1) # second derivative
    df["macd_zero_cross"]= np.where(macd_line > 0, 1, -1)

    # ── ATR 14 ──────────────────────────────────────────────────────────────
    tr = pd.concat([
        high - low,
        (high - close.shift(1)).abs(),
        (low  - close.shift(1)).abs()
    ], axis=1).max(axis=1)
    atr14 = tr.ewm(alpha=1/14, min_periods=14, adjust=False).mean()
    df["atr_14"]   = atr14
    df["natr_14"]  = atr14 / close          # normalised ATR
    df["atr_ratio"]= atr14 / atr14.rolling(50).mean()  # current vs recent vol

    # ── Volatility regime ───────────────────────────────────────────────────
    df["vol_high"] = np.where(df["natr_14"] > df["natr_14"].rolling(100).median(), 1, 0)

    # ── Price momentum ──────────────────────────────────────────────────────
    df["ret_1"]  = close.pct_change(1)
    df["ret_3"]  = close.pct_change(3)
    df["ret_8"]  = close.pct_change(8)
    df["ret_16"] = close.pct_change(16)

    # Scaled by ATR so the model sees normalised moves
    df["ret_1_atr"]  = df["ret_1"]  / (atr14 / close).replace(0, np.nan)
    df["ret_3_atr"]  = df["ret_3"]  / (atr14 / close).replace(0, np.nan)
    df["ret_8_atr"]  = df["ret_8"]  / (atr14 / close).replace(0, np.nan)

    # ── EMAs & trend structure ───────────────────────────────────────────────
    ema8  = close.ewm(span=8,  adjust=False).mean()
    ema21 = close.ewm(span=21, adjust=False).mean()
    ema50 = close.ewm(span=50, adjust=False).mean()
    ema100= close.ewm(span=100,adjust=False).mean()

    df["ema8_21_spread"] = (ema8 - ema21) / close
    df["ema21_50_spread"]= (ema21 - ema50) / close
    df["ema50_100_spread"]= (ema50 - ema100) / close

    df["price_vs_ema21"] = (close - ema21) / close
    df["price_vs_ema50"] = (close - ema50) / close

    df["trend_align"] = np.where(
        (ema8 > ema21) & (ema21 > ema50), 1,
        np.where((ema8 < ema21) & (ema21 < ema50), -1, 0)
    )

    # ── Bollinger Bands (20, 2) ──────────────────────────────────────────────
    bb_mid  = close.rolling(20).mean()
    bb_std  = close.rolling(20).std(ddof=0)
    bb_up   = bb_mid + 2 * bb_std
    bb_lo   = bb_mid - 2 * bb_std
    bb_bw   = (bb_up - bb_lo) / bb_mid          # bandwidth
    bb_pct  = (close - bb_lo) / (bb_up - bb_lo) # %B

    df["bb_pct"]      = bb_pct
    df["bb_bw"]       = bb_bw
    df["bb_bw_ratio"] = bb_bw / bb_bw.rolling(50).mean()  # squeeze detector
    df["bb_upper_touch"] = np.where(close >= bb_up, 1, 0)
    df["bb_lower_touch"] = np.where(close <= bb_lo, 1, 0)

    # ── Stochastic %K %D (14, 3) ────────────────────────────────────────────
    lo14   = low.rolling(14).min()
    hi14   = high.rolling(14).max()
    stoch_k = 100 * (close - lo14) / (hi14 - lo14).replace(0, np.nan)
    stoch_d = stoch_k.rolling(3).mean()

    df["stoch_k"]      = stoch_k
    df["stoch_d"]      = stoch_d
    df["stoch_kd_diff"]= stoch_k - stoch_d
    df["stoch_ob"]     = np.where(stoch_k > 80, 1, 0)
    df["stoch_os"]     = np.where(stoch_k < 20, 1, 0)

    # ── Candle structure ────────────────────────────────────────────────────
    body      = (close - open_).abs()
    candle_rng= (high - low).replace(0, np.nan)
    df["body_ratio"]   = body / candle_rng        # body vs full range
    df["upper_wick"]   = (high - pd.concat([close, open_], axis=1).max(axis=1)) / candle_rng
    df["lower_wick"]   = (pd.concat([close, open_], axis=1).min(axis=1) - low) / candle_rng
    df["candle_dir"]   = np.where(close > open_, 1, -1)
    df["candle_dir_3"] = df["candle_dir"].rolling(3).sum()  # short-term bias

    # ── Volume-less momentum oscillator (Williams %R 14) ───────────────────
    df["williams_r"] = -100 * (hi14 - close) / (hi14 - lo14).replace(0, np.nan)

    # ── RSI x MACD composite signal ─────────────────────────────────────────
    df["rsi_macd_bull"] = np.where(
        (df["rsi_14"] > 50) & (macd_hist > 0), 1,
        np.where((df["rsi_14"] < 50) & (macd_hist < 0), -1, 0)
    )

    # ── Divergence proxy: price vs RSI direction (3-bar) ────────────────────
    price_dir3 = np.sign(close.diff(3))
    rsi_dir3   = np.sign(df["rsi_14"].diff(3))
    df["rsi_div"] = np.where(price_dir3 != rsi_dir3, 1, 0)

    # ── Mean-reversion signal: distance from 50-bar mean normalised by ATR ──
    sma50 = close.rolling(50).mean()
    df["zscore_50"] = (close - sma50) / (close.rolling(50).std(ddof=0).replace(0, np.nan))

    df["mean_rev_long"]  = np.where(df["zscore_50"] < -1.5, 1, 0)
    df["mean_rev_short"] = np.where(df["zscore_50"] >  1.5, 1, 0)

    # ── Interaction features ─────────────────────────────────────────────────
    df["rsi_bb_pct"]        = df["rsi_14"] * df["bb_pct"]
    df["macd_hist_rsi_mid"] = df["macd_hist"] * df["rsi_mid"]
    df["stoch_rsi"]         = df["stoch_k"] * df["rsi_14"] / 1e4  # normalised product

    # ── Lag features (avoid lookahead) ──────────────────────────────────────
    for lag in [1, 2, 4, 8]:
        df[f"rsi_lag{lag}"]       = df["rsi_14"].shift(lag)
        df[f"macd_hist_lag{lag}"] = df["macd_hist"].shift(lag)
        df[f"ret_lag{lag}"]       = df["ret_1"].shift(lag)

    # ── Hour-of-day & day-of-week cyclic encoding ───────────────────────────
    if hasattr(df.index, "hour"):
        hour = df.index.hour
        dow  = df.index.dayofweek
        df["hour_sin"] = np.sin(2 * np.pi * hour / 24)
        df["hour_cos"] = np.cos(2 * np.pi * hour / 24)
        df["dow_sin"]  = np.sin(2 * np.pi * dow  / 5)
        df["dow_cos"]  = np.cos(2 * np.pi * dow  / 5)

    # ── Final fill ───────────────────────────────────────────────────────────
    df = df.bfill().ffill()

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "NZD/USD RSI-MACD Gradient Boost Risk-Adjusted",
        "model_type":       "GradientBoostingClassifier",
        "model_params":     {
            "n_estimators":     600,
            "max_depth":        4,
            "learning_rate":    0.03,
            "subsample":        0.75,
            "max_features":     "sqrt",
            "min_samples_leaf": 20,
            "min_samples_split":40,
            "warm_start":       False,
        },
        "signal_threshold": 0.56,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [21, 21],
        "min_atr":          0.0002,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective": (
            "Maximize risk-adjusted return (Sharpe/Calmar) using a deep "
            "GradientBoostingClassifier with many slow-learning trees and "
            "aggressive regularisation (min_samples_leaf=20, subsample=0.75). "
            "Feature set deliberately differs from prior RSI+BB+Stoch attempts "
            "by adding: ATR-normalised returns, z-score mean-reversion signals, "
            "RSI divergence proxy, Williams %R, candle structure ratios, cyclic "
            "time encoding, and interaction/lag features to give the model richer "
            "multi-timeframe context. SL=0.5%/TP=1% gives 1:2 RR aligned with "
            "maximising Sharpe."
        ),
        "notes": (
            "Prior PF=1.35 / ret=+18.36% used standard RSI+MACD+BB+Stoch without "
            "ATR normalisation or divergence detection. This version adds z-score "
            "mean-reversion context, candle structure, and temporal encoding to "
            "reduce false positives. session_filter=[21,21] is intentionally "
            "narrow — set to None if you want 24h coverage. min_atr=0.0002 "
            "avoids dead-market signals."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-07 01:38:25
# Model            : XGBoost
# Feature Eng.     : EMA crossover trend (9/21) with RSI 14 confirmation on EURUSD 15min + Auto-add features: ON
# Signal / Entry   : —
# Optimization     : —
# Risk Mgmt        : —
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# QUANTIFY ME — STRATEGY MODULE
# EMA Crossover + RSI Confirmation (XGBoost, Sharpe Optimization)
# ============================================================

import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/EURUSD_15min.parquet"
START_DATE = "2026-03-28"
END_DATE   = "2026-04-25"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7


# ============================================================
# SECTION 1 — FEATURE ENGINEERING
# ============================================================

def feature_engineering(df, close, open_, high, low):
    """
    Add EMA crossover trend features + RSI confirmation.
    
    Features:
      - EMA 9 and EMA 21 for trend direction
      - RSI 14 for momentum confirmation
      - EMA crossover signal
      - Price deviation from EMA 21
      - High/Low proximity ratios
      - Volume-based volatility (NATR)
    """
    
    # EMA 9 and EMA 21 for trend
    df['ema_9'] = close.ewm(span=9, adjust=False).mean()
    df['ema_21'] = close.ewm(span=21, adjust=False).mean()
    df['ema_crossover'] = np.where(df['ema_9'] > df['ema_21'], 1, -1)
    
    # EMA crossover signal (1 when 9 crosses above 21, -1 when crosses below)
    df['ema_cross_signal'] = df['ema_crossover'].diff().fillna(0)
    df['ema_cross_signal'] = np.where(df['ema_cross_signal'] != 0, df['ema_cross_signal'], 0)
    
    # Price deviation from EMA 21 (normalized)
    df['price_ema_deviation'] = (close - df['ema_21']) / df['ema_21']
    
    # RSI 14 for momentum confirmation
    delta = close.diff()
    gain = np.where(delta > 0, delta, 0)
    loss = np.where(delta < 0, -delta, 0)
    
    avg_gain = pd.Series(gain, index=close.index).ewm(span=14, adjust=False).mean()
    avg_loss = pd.Series(loss, index=close.index).ewm(span=14, adjust=False).mean()
    
    rs = avg_gain / (avg_loss + 1e-10)
    df['rsi_14'] = 100 - (100 / (1 + rs))
    
    # RSI signal: overbought/oversold
    df['rsi_overbought'] = np.where(df['rsi_14'] > 70, 1, 0)
    df['rsi_oversold'] = np.where(df['rsi_14'] < 30, 1, 0)
    
    # High/Low proximity (distance from recent extremes)
    df['high_20'] = high.rolling(window=20).max()
    df['low_20'] = low.rolling(window=20).min()
    df['price_position'] = (close - df['low_20']) / (df['high_20'] - df['low_20'] + 1e-10)
    
    # NATR (Normalized ATR) for volatility
    atr_period = 14
    tr1 = high - low
    tr2 = np.abs(high - close.shift(1))
    tr3 = np.abs(low - close.shift(1))
    tr = np.maximum(tr1, np.maximum(tr2, tr3))
    atr = pd.Series(tr, index=close.index).rolling(window=atr_period).mean()
    df['natr'] = (atr / close) * 100
    
    # EMA momentum (rate of change in EMA)
    df['ema_9_roc'] = df['ema_9'].pct_change(periods=3)
    df['ema_21_roc'] = df['ema_21'].pct_change(periods=3)
    
    # Close relative to open (intrabar direction)
    df['close_above_open'] = np.where(close > open_, 1, 0)
    
    # Volume-based features (if available; otherwise skip)
    if 'volume' in df.columns:
        df['volume_ma'] = df['volume'].rolling(window=20).mean()
        df['volume_ratio'] = df['volume'] / (df['volume_ma'] + 1e-10)
    else:
        df['volume_ratio'] = 1.0
    
    # Fill NaN from indicator warm-up
    df = df.bfill().ffill()
    
    return df


# ============================================================
# SECTION 2 — STRATEGY CONFIG
# ============================================================

def strategy_config():
    """
    XGBoost strategy optimized for Sharpe ratio on EMA/RSI signals.
    
    Hyperparameters tuned for:
      - Fast learning (learning_rate=0.08)
      - Shallow trees (max_depth=4) to avoid overfitting on 15min data
      - Moderate boosting (n_estimators=250) for good generalization
      - Regularization (subsample=0.85, colsample_bytree=0.8)
      - Balanced class weights via scale_pos_weight
    
    Signal threshold 0.55 chosen to be moderately selective while maintaining
    good trade frequency on the EMA crossover setup.
    """
    
    return {
        # Model specification
        "model_type": "XGBClassifier",
        "model_params": {
            "n_estimators": 250,
            "max_depth": 4,
            "learning_rate": 0.08,
            "subsample": 0.85,
            "colsample_bytree": 0.8,
            "min_child_weight": 1,
            "gamma": 0.5,
            "reg_alpha": 0.1,
            "reg_lambda": 1.0,
            "random_state": 42,
            "verbosity": 0,
        },
        
        # Entry signal
        "signal_threshold": 0.55,
        
        # Position management
        "direction": "both",
        "max_positions": 1,
        "on_opposite": "reverse",
        "cooldown": 0,
        
        # Risk management
        "stop_loss": 0.008,
        "take_profit": 0.015,
        
        # Filters
        "session_filter": None,
        "min_atr": None,
        "trend_filter": None,
        
        # Target
        "target_horizon": 4,
        
        # Metadata
        "title": "EMA Crossover + RSI Confirmation (XGBoost)",
        "objective": "Maximize Sharpe ratio with EMA 9/21 trend + RSI 14 momentum confirmation",
        "notes": (
            "Strategy uses fast EMA (9) crossover above/below slow EMA (21) "
            "as primary trend signal, confirmed by RSI 14 momentum. "
            "XGBoost learns non-linear interactions between these features. "
            "Moderate SL/TP (0.8%/1.5%) and bidirectional trading for scalping efficiency. "
            "Optimized for EURUSD 15min with 70/30 train/test split."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 01:52:32
# Model            : XGBoost
# Feature Eng.     : BB (20,2.0), RSI 14, Stochastic (14,3) + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCHF_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # ── RSI 14 ──────────────────────────────────────────────────────────────
    period_rsi = 14
    delta = close.diff()
    gain = delta.clip(lower=0)
    loss = -delta.clip(upper=0)
    avg_gain = gain.ewm(com=period_rsi - 1, min_periods=period_rsi).mean()
    avg_loss = loss.ewm(com=period_rsi - 1, min_periods=period_rsi).mean()
    rs = avg_gain / avg_loss.replace(0, np.nan)
    df["rsi_14"] = 100 - (100 / (1 + rs))

    # ── Bollinger Bands (20, 2) ──────────────────────────────────────────────
    bb_period = 20
    bb_std = 2.0
    bb_mid = close.rolling(bb_period).mean()
    bb_std_val = close.rolling(bb_period).std(ddof=0)
    bb_upper = bb_mid + bb_std * bb_std_val
    bb_lower = bb_mid - bb_std * bb_std_val
    df["bb_mid"] = bb_mid
    df["bb_upper"] = bb_upper
    df["bb_lower"] = bb_lower
    df["bb_width"] = (bb_upper - bb_lower) / bb_mid
    bb_range = (bb_upper - bb_lower).replace(0, np.nan)
    df["bb_pct"] = (close - bb_lower) / bb_range

    # ── Stochastic Oscillator (K=14, D=3) ───────────────────────────────────
    stoch_k_period = 14
    stoch_d_period = 3
    lowest_low = low.rolling(stoch_k_period).min()
    highest_high = high.rolling(stoch_k_period).max()
    stoch_range = (highest_high - lowest_low).replace(0, np.nan)
    df["stoch_k"] = 100 * (close - lowest_low) / stoch_range
    df["stoch_d"] = df["stoch_k"].rolling(stoch_d_period).mean()
    df["stoch_kd_diff"] = df["stoch_k"] - df["stoch_d"]

    # ── Additional derived features ──────────────────────────────────────────

    # RSI momentum & zone flags
    df["rsi_lag1"] = df["rsi_14"].shift(1)
    df["rsi_momentum"] = df["rsi_14"] - df["rsi_lag1"]
    df["rsi_oversold"] = np.where(df["rsi_14"] < 30, 1, 0)
    df["rsi_overbought"] = np.where(df["rsi_14"] > 70, 1, 0)

    # BB squeeze: width below rolling 20-bar median of bb_width
    bb_width_median = df["bb_width"].rolling(20).median()
    df["bb_squeeze"] = np.where(df["bb_width"] < bb_width_median, 1, 0)

    # BB position zone
    df["bb_below_lower"] = np.where(close < bb_lower, 1, 0)
    df["bb_above_upper"] = np.where(close > bb_upper, 1, 0)

    # Stochastic zone flags
    df["stoch_oversold"] = np.where(df["stoch_k"] < 20, 1, 0)
    df["stoch_overbought"] = np.where(df["stoch_k"] > 80, 1, 0)

    # Price momentum (rate of change)
    df["roc_4"] = close.pct_change(4)
    df["roc_8"] = close.pct_change(8)
    df["roc_16"] = close.pct_change(16)

    # ATR (14-bar) for volatility context
    atr_period = 14
    tr = pd.concat([
        high - low,
        (high - close.shift(1)).abs(),
        (low - close.shift(1)).abs()
    ], axis=1).max(axis=1)
    df["atr_14"] = tr.ewm(com=atr_period - 1, min_periods=atr_period).mean()
    df["natr_14"] = df["atr_14"] / close

    # EMA crossover signals
    ema_fast = close.ewm(span=8, min_periods=8).mean()
    ema_slow = close.ewm(span=21, min_periods=21).mean()
    df["ema_fast"] = ema_fast
    df["ema_slow"] = ema_slow
    df["ema_cross"] = ema_fast - ema_slow
    df["ema_cross_sign"] = np.where(df["ema_cross"] > 0, 1, -1)

    # SMA 50 trend context
    df["sma_50"] = close.rolling(50).mean()
    df["close_vs_sma50"] = (close - df["sma_50"]) / df["sma_50"]

    # Candle body and direction
    df["candle_body"] = (close - open_).abs()
    df["candle_range"] = (high - low).replace(0, np.nan)
    df["body_ratio"] = df["candle_body"] / df["candle_range"]
    df["candle_dir"] = np.where(close >= open_, 1, -1)

    # Volume-proxy: range relative to rolling average range
    df["rel_range"] = (high - low) / (high - low).rolling(20).mean()

    # Lag features for RSI, stoch_k, bb_pct
    for lag in [1, 2, 3]:
        df[f"rsi_14_lag{lag}"] = df["rsi_14"].shift(lag)
        df[f"stoch_k_lag{lag}"] = df["stoch_k"].shift(lag)
        df[f"bb_pct_lag{lag}"] = df["bb_pct"].shift(lag)
        df[f"ema_cross_lag{lag}"] = df["ema_cross"].shift(lag)

    # Divergence proxy: price making new high but RSI not
    price_high_4 = close.rolling(4).max()
    rsi_high_4 = df["rsi_14"].rolling(4).max()
    df["bearish_div_proxy"] = np.where(
        (close >= price_high_4.shift(1)) & (df["rsi_14"] < rsi_high_4.shift(1)), 1, 0
    )
    price_low_4 = close.rolling(4).min()
    rsi_low_4 = df["rsi_14"].rolling(4).min()
    df["bullish_div_proxy"] = np.where(
        (close <= price_low_4.shift(1)) & (df["rsi_14"] > rsi_low_4.shift(1)), 1, 0
    )

    # Combined confluence signals
    df["long_confluence"] = np.where(
        (df["rsi_14"] < 45) & (df["stoch_k"] < 50) & (df["bb_pct"] < 0.5), 1, 0
    )
    df["short_confluence"] = np.where(
        (df["rsi_14"] > 55) & (df["stoch_k"] > 50) & (df["bb_pct"] > 0.5), 1, 0
    )

    # Fill NaN from warm-up
    df = df.bfill().ffill()
    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title": "USD/CHF Stoch+BB+RSI Mean-Reversion (XGBoost)",
        "model_type": "XGBClassifier",
        "model_params": {
            "n_estimators": 400,
            "max_depth": 4,
            "learning_rate": 0.04,
            "subsample": 0.8,
            "colsample_bytree": 0.75,
            "min_child_weight": 5,
            "gamma": 0.1,
            "reg_alpha": 0.05,
            "reg_lambda": 1.5,
            "objective": "binary:logistic",
            "tree_method": "hist",
            "random_state": 42,
            "n_jobs": -1,
        },
        "signal_threshold": 0.55,
        "direction": "both",
        "stop_loss": 0.005,
        "take_profit": 0.01,
        "cooldown": 0,
        "max_positions": 1,
        "on_opposite": "reverse",
        "session_filter": [7, 17],
        "min_atr": None,
        "trend_filter": None,
        "target_horizon": 4,
        "objective": (
            "Maximize risk-adjusted return (Sharpe/Calmar) on USD/CHF 15-min data. "
            "Uses Stochastic (14,3), Bollinger Bands (20,2), and RSI-14 as core features "
            "with confluence signals, divergence proxies, and EMA crossover context. "
            "XGBoost chosen for its strong performance on tabular data with regularization "
            "parameters (gamma, alpha, lambda) tuned to reduce overfitting on short date "
            "ranges. SL=0.5%/TP=1.0% gives 1:2 R:R ratio. Session filter [7,17] UTC targets "
            "London/NY overlap for higher-quality moves. Signal threshold 0.55 filters noise "
            "while preserving trade frequency."
        ),
        "notes": (
            "Feature set combines mean-reversion indicators (RSI, Stochastic, BB percentile) "
            "with trend context (EMA cross, SMA50 distance) and volatility measures (ATR, "
            "BB width/squeeze). Lag features (1-3 bars) capture recent indicator momentum. "
            "Bullish/bearish divergence proxies add signal quality. Shallow trees (max_depth=4) "
            "with high n_estimators and slow learning rate reduce variance. Colsample and "
            "subsample add stochastic regularization."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 03:08:55
# Model            : Gradient Boosting
# Feature Eng.     : SMA (20,50,200), BB (20,2.0), RSI 14, MACD (12,26,9), ATR 14 + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/EURUSD_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):

    # ── SMA 20, 50, 200 + distance from close ─────────────────────────────
    for p in [20, 50, 200]:
        sma = close.rolling(p).mean()
        df[f"sma_{p}"]    = sma
        df[f"dm_sma_{p}"] = (close - sma) / sma

    # ── Bollinger Bands (20, 2) ────────────────────────────────────────────
    bb_mid   = close.rolling(20).mean()
    bb_std   = close.rolling(20).std(ddof=0)
    bb_upper = bb_mid + 2.0 * bb_std
    bb_lower = bb_mid - 2.0 * bb_std
    df["bb_width"] = (bb_upper - bb_lower) / bb_mid
    denom          = bb_upper - bb_lower
    df["bb_pct"]   = np.where(denom != 0, (close - bb_lower) / denom, 0.5)

    # ── RSI 14 ────────────────────────────────────────────────────────────
    delta    = close.diff()
    gain     = delta.clip(lower=0)
    loss     = (-delta).clip(lower=0)
    avg_gain = gain.ewm(com=13, min_periods=14).mean()
    avg_loss = loss.ewm(com=13, min_periods=14).mean()
    rs       = avg_gain / avg_loss.replace(0, np.nan)
    df["rsi_14"] = 100 - (100 / (1 + rs))

    # ── MACD (12, 26, 9) ──────────────────────────────────────────────────
    ema_fast        = close.ewm(span=12, adjust=False).mean()
    ema_slow        = close.ewm(span=26, adjust=False).mean()
    macd_line       = ema_fast - ema_slow
    signal_line     = macd_line.ewm(span=9, adjust=False).mean()
    df["macd_line"] = macd_line
    df["macd_sig"]  = signal_line
    df["macd_hist"] = macd_line - signal_line

    # ── ATR 14 + NATR ─────────────────────────────────────────────────────
    hl    = high - low
    hc    = (high - close.shift(1)).abs()
    lc    = (low  - close.shift(1)).abs()
    tr    = pd.concat([hl, hc, lc], axis=1).max(axis=1)
    atr   = tr.ewm(com=13, min_periods=14).mean()
    df["atr_14"]  = atr
    df["natr"]    = atr / close

    # ── Price momentum / rate-of-change ───────────────────────────────────
    for p in [4, 8, 16, 32]:
        df[f"roc_{p}"] = close.pct_change(p)

    # ── Candle body and wick features ─────────────────────────────────────
    body          = (close - open_).abs()
    candle_range  = (high - low).replace(0, np.nan)
    df["body_ratio"]   = body / candle_range
    df["upper_wick"]   = (high - pd.concat([close, open_], axis=1).max(axis=1)) / candle_range
    df["lower_wick"]   = (pd.concat([close, open_], axis=1).min(axis=1) - low)  / candle_range
    df["body_dir"]     = np.sign(close - open_)

    # ── Volume-normalised (uses candle range as proxy if no volume col) ───
    # Rolling z-score of close
    roll_mean = close.rolling(20).mean()
    roll_std  = close.rolling(20).std(ddof=0).replace(0, np.nan)
    df["close_zscore_20"] = (close - roll_mean) / roll_std

    # ── RSI divergence proxy ──────────────────────────────────────────────
    df["rsi_delta_4"]  = df["rsi_14"].diff(4)
    df["price_delta_4"] = close.pct_change(4)
    df["rsi_price_div"] = df["rsi_delta_4"] - (df["price_delta_4"] * 100)

    # ── MACD histogram slope ──────────────────────────────────────────────
    df["macd_hist_slope"] = df["macd_hist"].diff(2)

    # ── SMA crossover signals ─────────────────────────────────────────────
    df["sma20_vs_50"]   = np.where(df["sma_20"] > df["sma_50"],  1.0, -1.0)
    df["sma50_vs_200"]  = np.where(df["sma_50"] > df["sma_200"], 1.0, -1.0)

    # ── Volatility regime (ATR percentile proxy) ──────────────────────────
    atr_roll_min = atr.rolling(96).min()
    atr_roll_max = atr.rolling(96).max()
    atr_range    = (atr_roll_max - atr_roll_min).replace(0, np.nan)
    df["atr_pctile_96"] = (atr - atr_roll_min) / atr_range

    # ── Stochastic oscillator %K, %D ──────────────────────────────────────
    low_14  = low.rolling(14).min()
    high_14 = high.rolling(14).max()
    stoch_k = 100 * (close - low_14) / (high_14 - low_14).replace(0, np.nan)
    stoch_d = stoch_k.rolling(3).mean()
    df["stoch_k"] = stoch_k
    df["stoch_d"] = stoch_d
    df["stoch_diff"] = stoch_k - stoch_d

    # ── Rolling high/low breakout distance ────────────────────────────────
    df["dist_hi_20"] = (high.rolling(20).max() - close) / close
    df["dist_lo_20"] = (close - low.rolling(20).min())  / close

    # ── Hour-of-day and day-of-week cyclical features ─────────────────────
    hour = pd.Series(df.index.hour,        index=df.index, dtype=float)
    dow  = pd.Series(df.index.dayofweek,   index=df.index, dtype=float)
    df["hour_sin"] = np.sin(2 * np.pi * hour / 24)
    df["hour_cos"] = np.cos(2 * np.pi * hour / 24)
    df["dow_sin"]  = np.sin(2 * np.pi * dow  / 5)
    df["dow_cos"]  = np.cos(2 * np.pi * dow  / 5)

    # ── Fill NaN from indicator warm-up ───────────────────────────────────
    df = df.bfill().ffill()

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "EUR/USD Gradient Boost SMA+RSI+MACD Swing",
        "model_type":       "GradientBoostingClassifier",
        "model_params":     {
            "n_estimators":       400,
            "max_depth":          4,
            "learning_rate":      0.04,
            "subsample":          0.75,
            "min_samples_leaf":   20,
            "min_samples_split":  30,
            "max_features":       "sqrt",
            "validation_fraction": 0.1,
            "n_iter_no_change":   30,
            "tol":                1e-4,
        },
        "signal_threshold": 0.55,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [7, 18],
        "min_atr":          0.0002,
        "trend_filter":     "sma_50",
        "target_horizon":   4,
        "objective":        (
            "Maximize risk-adjusted return (Sharpe / Calmar) on EUR/USD 15-min. "
            "GradientBoostingClassifier chosen for robustness to noisy FX features and "
            "good probability calibration. Shallow trees (depth 4), high n_estimators with "
            "early stopping prevent overfitting. Subsample=0.75 adds stochasticity. "
            "SL=0.5%, TP=1.0% gives 1:2 R:R. Session filter 07-18 UTC captures London+NY overlap. "
            "min_atr filters out flat/illiquid periods. sma_50 trend filter aligns trades with "
            "medium-term momentum. Threshold 0.55 balances precision vs recall."
        ),
        "notes":            (
            "Features: SMA(20,50,200) with distance ratios, BB(20,2) width+pct, RSI-14, "
            "MACD(12,26,9) line/signal/hist + slope, ATR-14 + NATR, ROC(4,8,16,32), "
            "candle body/wick ratios, close z-score, RSI-price divergence proxy, "
            "stochastic %K/%D, rolling high/low breakout distances, "
            "SMA crossover flags, ATR percentile regime, hour/DOW cyclical encodings. "
            "on_opposite=reverse means a strong counter-signal immediately flips the position, "
            "reducing idle time and capturing reversals within the London-NY session."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 02:19:50
# Model            : XGBoost
# Feature Eng.     : EMA (50,200), ATR 14 + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/EURUSD_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # ── EMA 50 / 200 and distance features ──────────────────────────────
    ema_50  = close.ewm(span=50,  adjust=False).mean()
    ema_200 = close.ewm(span=200, adjust=False).mean()

    df["ema_50"]    = ema_50
    df["ema_200"]   = ema_200
    df["dm_ema_50"]  = (close - ema_50)  / ema_50
    df["dm_ema_200"] = (close - ema_200) / ema_200

    # EMA cross signal: +1 when ema_50 > ema_200, -1 otherwise
    df["ema_cross"] = np.where(ema_50 > ema_200, 1.0, -1.0)

    # Spread between the two EMAs, normalised by price
    df["ema_spread"] = (ema_50 - ema_200) / close

    # Rate of change of ema_spread (momentum of the cross)
    df["ema_spread_roc"] = df["ema_spread"].diff(4)

    # ── ATR 14 & NATR ───────────────────────────────────────────────────
    prev_close = close.shift(1)
    tr = pd.concat([
        high - low,
        (high - prev_close).abs(),
        (low  - prev_close).abs()
    ], axis=1).max(axis=1)

    atr  = tr.ewm(span=14, adjust=False).mean()
    natr = atr / close

    df["atr"]  = atr
    df["natr"] = natr

    # ── RSI 14 ───────────────────────────────────────────────────────────
    delta  = close.diff()
    gain   = delta.clip(lower=0)
    loss   = (-delta).clip(lower=0)
    avg_gain = gain.ewm(span=14, adjust=False).mean()
    avg_loss = loss.ewm(span=14, adjust=False).mean()
    rs  = avg_gain / (avg_loss + 1e-12)
    rsi = 100.0 - (100.0 / (1.0 + rs))
    df["rsi_14"] = rsi
    # Normalised RSI centred around 0
    df["rsi_norm"] = (rsi - 50.0) / 50.0

    # ── MACD (12/26/9) ───────────────────────────────────────────────────
    ema_12   = close.ewm(span=12, adjust=False).mean()
    ema_26   = close.ewm(span=26, adjust=False).mean()
    macd     = ema_12 - ema_26
    signal   = macd.ewm(span=9,  adjust=False).mean()
    df["macd"]        = macd / close
    df["macd_signal"] = signal / close
    df["macd_hist"]   = (macd - signal) / close

    # ── Bollinger Bands (20, 2σ) ─────────────────────────────────────────
    bb_mid  = close.rolling(20).mean()
    bb_std  = close.rolling(20).std(ddof=0)
    bb_up   = bb_mid + 2.0 * bb_std
    bb_lo   = bb_mid - 2.0 * bb_std
    df["bb_width"] = (bb_up - bb_lo) / (bb_mid + 1e-12)
    df["bb_pct"]   = (close - bb_lo)  / (bb_up - bb_lo + 1e-12)

    # ── Price momentum (log-returns at multiple horizons) ────────────────
    for lag in [1, 4, 8, 16]:
        df[f"logret_{lag}"] = np.log(close / close.shift(lag))

    # ── Volume-less volatility proxy: high-low range / ATR ───────────────
    df["hl_range_norm"] = (high - low) / (atr + 1e-12)

    # ── Candle body and shadow ratios ────────────────────────────────────
    body  = (close - open_).abs()
    range_ = (high - low + 1e-12)
    df["body_ratio"]       = body / range_
    df["upper_shadow_ratio"] = (high - pd.concat([close, open_], axis=1).max(axis=1)) / range_
    df["lower_shadow_ratio"] = (pd.concat([close, open_], axis=1).min(axis=1) - low) / range_

    # ── Trend strength: close relative to recent N-bar high/low ──────────
    for window in [20, 50]:
        roll_hi = high.rolling(window).max()
        roll_lo = low.rolling(window).min()
        denom   = (roll_hi - roll_lo + 1e-12)
        df[f"close_rank_{window}"] = (close - roll_lo) / denom

    # ── EMA 50 slope (rate of change) ────────────────────────────────────
    df["ema_50_slope"]  = ema_50.diff(4)  / (close + 1e-12)
    df["ema_200_slope"] = ema_200.diff(8) / (close + 1e-12)

    # ── Hour-of-day and day-of-week as cyclic features ───────────────────
    if hasattr(df.index, "hour"):
        hour = df.index.hour
        dow  = df.index.dayofweek
        df["hour_sin"] = np.sin(2.0 * np.pi * hour / 24.0)
        df["hour_cos"] = np.cos(2.0 * np.pi * hour / 24.0)
        df["dow_sin"]  = np.sin(2.0 * np.pi * dow  / 5.0)
        df["dow_cos"]  = np.cos(2.0 * np.pi * dow  / 5.0)

    # ── Fill NaNs from indicator warm-up ────────────────────────────────
    df = df.bfill().ffill()

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "EUR/USD EMA Cross + ATR Momentum (XGBoost)",
        "model_type":       "XGBClassifier",
        "model_params": {
            "n_estimators":        400,
            "max_depth":           4,
            "learning_rate":       0.04,
            "subsample":           0.75,
            "colsample_bytree":    0.75,
            "min_child_weight":    3,
            "gamma":               0.1,
            "reg_alpha":           0.05,
            "reg_lambda":          1.5,
            "objective":           "binary:logistic",
            "random_state":        42,
            "n_jobs":              -1,
        },
        "signal_threshold": 0.55,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [6, 20],
        "min_atr":          0.0002,
        "trend_filter":     "sma_50",
        "target_horizon":   4,
        "objective": (
            "Maximize risk-adjusted return (Sharpe) by combining EMA crossover "
            "trend regime with ATR-normalised volatility, RSI, MACD, and Bollinger "
            "features. XGBoost hyperparameters tuned for bias-variance balance: "
            "moderate depth (4), aggressive shrinkage (lr=0.04), column/row "
            "subsampling, and L1/L2 regularisation. SL=0.5% / TP=1.0% targets a "
            "2:1 reward-risk ratio. Session filter [6,20] UTC focuses on the "
            "liquid London/NY overlap. min_atr filters dead markets."
        ),
        "notes": (
            "EMA 50/200 cross provides the macro trend regime; distance features "
            "capture how far price has stretched from trend. ATR/NATR quantifies "
            "volatility regime. RSI, MACD histogram, and BB %b add mean-reversion "
            "and momentum context. Candle body ratios encode micro-structure. "
            "Cyclic time features allow the model to learn intraday seasonality. "
            "target_horizon=4 bars (1 hour on 15-min data) balances trade frequency "
            "against predictability. on_opposite=reverse reduces idle time and "
            "captures trend continuation efficiently."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 01:53:28
# Model            : Gradient Boosting
# Feature Eng.     : BB (20,2.0), ATR 14 + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# Bollinger Bands Squeeze Breakout — GBP/USD 15-min
# ============================================================

# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/GBPUSD_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # ── Bollinger Bands (20, 2) ──────────────────────────────────────────────
    bb_period = 20
    bb_std    = 2.0

    bb_mid   = close.rolling(bb_period).mean()
    bb_sigma = close.rolling(bb_period).std(ddof=0)
    bb_upper = bb_mid + bb_std * bb_sigma
    bb_lower = bb_mid - bb_std * bb_sigma

    bb_width = (bb_upper - bb_lower) / bb_mid
    bb_pct   = (close - bb_lower) / (bb_upper - bb_lower)

    df["bb_mid"]   = bb_mid
    df["bb_upper"] = bb_upper
    df["bb_lower"] = bb_lower
    df["bb_width"] = bb_width
    df["bb_pct"]   = bb_pct

    # ── ATR (14) & NATR ─────────────────────────────────────────────────────
    atr_period = 14
    prev_close = close.shift(1)
    tr = pd.concat([
        high - low,
        (high - prev_close).abs(),
        (low  - prev_close).abs()
    ], axis=1).max(axis=1)
    atr  = tr.ewm(span=atr_period, min_periods=atr_period, adjust=False).mean()
    natr = atr / close

    df["atr"]  = atr
    df["natr"] = natr

    # ── Squeeze detection ────────────────────────────────────────────────────
    # Squeeze = BB width is in the bottom quartile over a 50-bar lookback
    bb_width_min = bb_width.rolling(50).min()
    bb_width_max = bb_width.rolling(50).max()
    bb_width_norm = (bb_width - bb_width_min) / (bb_width_max - bb_width_min + 1e-12)

    df["bb_width_norm"] = bb_width_norm
    df["squeeze"]       = np.where(bb_width_norm < 0.25, 1.0, 0.0)

    # Squeeze released: was in squeeze 1 bar ago, now width is expanding
    bb_width_chg = bb_width.diff()
    df["squeeze_release"] = np.where(
        (df["squeeze"].shift(1) == 1.0) & (bb_width_chg > 0), 1.0, 0.0
    )

    # ── BB width momentum ────────────────────────────────────────────────────
    df["bb_width_chg"]    = bb_width_chg
    df["bb_width_chg_2"]  = bb_width.diff(2)
    df["bb_width_chg_5"]  = bb_width.diff(5)

    # ── Price position relative to bands ─────────────────────────────────────
    df["close_vs_mid"]   = close - bb_mid
    df["close_vs_upper"] = close - bb_upper
    df["close_vs_lower"] = close - bb_lower

    # ── Momentum & returns ───────────────────────────────────────────────────
    df["ret_1"]  = close.pct_change(1)
    df["ret_3"]  = close.pct_change(3)
    df["ret_5"]  = close.pct_change(5)
    df["ret_10"] = close.pct_change(10)
    df["ret_20"] = close.pct_change(20)

    # ── RSI (14) ─────────────────────────────────────────────────────────────
    rsi_period = 14
    delta = close.diff()
    gain  = delta.clip(lower=0)
    loss  = (-delta).clip(lower=0)
    avg_gain = gain.ewm(span=rsi_period, min_periods=rsi_period, adjust=False).mean()
    avg_loss = loss.ewm(span=rsi_period, min_periods=rsi_period, adjust=False).mean()
    rs  = avg_gain / (avg_loss + 1e-12)
    rsi = 100.0 - 100.0 / (1.0 + rs)
    df["rsi"] = rsi

    # RSI divergence proxy: price makes new low/high but RSI does not
    df["rsi_5_min"]   = rsi.rolling(5).min()
    df["close_5_min"] = close.rolling(5).min()
    df["rsi_5_max"]   = rsi.rolling(5).max()
    df["close_5_max"] = close.rolling(5).max()

    # ── MACD ─────────────────────────────────────────────────────────────────
    ema_fast   = close.ewm(span=12, adjust=False).mean()
    ema_slow   = close.ewm(span=26, adjust=False).mean()
    macd_line  = ema_fast - ema_slow
    macd_signal = macd_line.ewm(span=9, adjust=False).mean()
    macd_hist  = macd_line - macd_signal

    df["macd_line"]   = macd_line
    df["macd_signal"] = macd_signal
    df["macd_hist"]   = macd_hist
    df["macd_hist_chg"] = macd_hist.diff()

    # ── Volume-like proxy: bar range ─────────────────────────────────────────
    bar_range = high - low
    df["bar_range"]      = bar_range
    df["bar_range_norm"] = bar_range / (atr + 1e-12)

    # ── Candle body direction & size ─────────────────────────────────────────
    body = close - open_
    df["body"]       = body
    df["body_norm"]  = body / (atr + 1e-12)
    df["body_dir"]   = np.where(body > 0, 1.0, np.where(body < 0, -1.0, 0.0))

    # ── Upper / lower wick ───────────────────────────────────────────────────
    df["upper_wick"] = high - pd.concat([close, open_], axis=1).max(axis=1)
    df["lower_wick"] = pd.concat([close, open_], axis=1).min(axis=1) - low

    # ── SMA trend context ─────────────────────────────────────────────────────
    sma_50  = close.rolling(50).mean()
    sma_200 = close.rolling(200).mean()
    df["sma_50"]         = sma_50
    df["sma_200"]        = sma_200
    df["close_vs_sma50"] = (close - sma_50) / (sma_50 + 1e-12)
    df["sma50_vs_sma200"] = (sma_50 - sma_200) / (sma_200 + 1e-12)

    # ── Volatility regime ────────────────────────────────────────────────────
    natr_ma = natr.rolling(50).mean()
    df["natr_ratio"] = natr / (natr_ma + 1e-12)   # >1 = above-avg vol

    # ── Mean-reversion distance ───────────────────────────────────────────────
    df["z_score_20"] = (close - bb_mid) / (bb_sigma + 1e-12)

    # ── Rolling realized vol ─────────────────────────────────────────────────
    df["rvol_10"] = df["ret_1"].rolling(10).std()
    df["rvol_20"] = df["ret_1"].rolling(20).std()

    # ── ATR-normalised returns ────────────────────────────────────────────────
    df["ret_1_natr"] = df["ret_1"] / (natr + 1e-12)

    # ── Lagged features ───────────────────────────────────────────────────────
    for lag in [1, 2, 3, 5]:
        df[f"bb_pct_lag{lag}"]    = bb_pct.shift(lag)
        df[f"bb_width_lag{lag}"]  = bb_width.shift(lag)
        df[f"rsi_lag{lag}"]       = rsi.shift(lag)
        df[f"macd_hist_lag{lag}"] = macd_hist.shift(lag)

    # ── Hour-of-day (cyclical) ────────────────────────────────────────────────
    hour = pd.Series(df.index.hour, index=df.index).astype(float)
    df["hour_sin"] = np.sin(2 * np.pi * hour / 24.0)
    df["hour_cos"] = np.cos(2 * np.pi * hour / 24.0)

    # ── Day-of-week (cyclical) ────────────────────────────────────────────────
    dow = pd.Series(df.index.dayofweek, index=df.index).astype(float)
    df["dow_sin"] = np.sin(2 * np.pi * dow / 5.0)
    df["dow_cos"] = np.cos(2 * np.pi * dow / 5.0)

    # ── Fill NaN from warm-up ────────────────────────────────────────────────
    df = df.bfill().ffill()

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title": "GBP/USD BB Squeeze Breakout (GradientBoosting)",

        "model_type": "GradientBoostingClassifier",
        "model_params": {
            "n_estimators":      400,
            "max_depth":         4,
            "learning_rate":     0.04,
            "subsample":         0.75,
            "min_samples_leaf":  20,
            "min_samples_split": 40,
            "max_features":      "sqrt",
            "n_iter_no_change":  30,
            "validation_fraction": 0.1,
            "tol":               1e-4,
            "random_state":      42,
        },

        "signal_threshold": 0.55,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [6, 20],
        "min_atr":          0.0002,
        "trend_filter":     None,
        "target_horizon":   4,

        "objective": (
            "Maximize risk-adjusted return (Sharpe / Calmar). "
            "GradientBoostingClassifier chosen for its strong performance on "
            "tabular financial data with noisy labels. Shallow trees (max_depth=4) "
            "with shrinkage (lr=0.04) and subsample=0.75 reduce overfitting. "
            "Early stopping (n_iter_no_change=30) prevents over-training. "
            "SL=0.5%, TP=1.0% gives a 1:2 risk/reward ratio. "
            "Session filter 06-20 UTC captures London + New York overlap for GBP/USD."
        ),
        "notes": (
            "Core signal: BB squeeze (narrow band width) followed by expansion "
            "breakout, confirmed by MACD histogram direction and RSI. "
            "ATR filter ensures minimum volatility for entries. "
            "Lagged BB features capture the squeeze build-up dynamic. "
            "Z-score and normalized returns give the model mean-reversion context. "
            "Cyclical time features allow the model to learn intraday seasonality."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 01:40:19
# Model            : XGBoost
# Feature Eng.     : EMA (9,21), RSI 14 + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/AUDUSD_15min.parquet"
START_DATE = "2025-04-24"
END_DATE   = "2026-04-24"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # ── EMA 9 and EMA 21 ──────────────────────────────────────────────────
    ema_9  = close.ewm(span=9,  adjust=False).mean()
    ema_21 = close.ewm(span=21, adjust=False).mean()

    df["ema_9"]    = ema_9
    df["ema_21"]   = ema_21
    df["dm_ema_9"]  = (close - ema_9)  / ema_9
    df["dm_ema_21"] = (close - ema_21) / ema_21

    # EMA crossover signal: positive when fast > slow
    df["ema_cross"]       = ema_9 - ema_21
    df["ema_cross_prev"]  = df["ema_cross"].shift(1)
    # Binary: did a cross just occur?
    df["ema_cross_up"]    = np.where((df["ema_cross"] > 0) & (df["ema_cross_prev"] <= 0), 1, 0)
    df["ema_cross_down"]  = np.where((df["ema_cross"] < 0) & (df["ema_cross_prev"] >= 0), 1, 0)
    # Trend direction encoded as -1 / 1
    df["ema_trend"]       = np.where(ema_9 > ema_21, 1, -1)

    # ── RSI 14 ────────────────────────────────────────────────────────────
    delta  = close.diff()
    gain   = delta.clip(lower=0)
    loss   = (-delta).clip(lower=0)
    avg_gain = gain.ewm(com=13, adjust=False).mean()
    avg_loss = loss.ewm(com=13, adjust=False).mean()
    rs     = avg_gain / avg_loss.replace(0, np.nan)
    rsi_14 = 100 - (100 / (1 + rs))
    df["rsi_14"] = rsi_14

    # RSI regime flags
    df["rsi_oversold"]   = np.where(rsi_14 < 30, 1, 0)
    df["rsi_overbought"] = np.where(rsi_14 > 70, 1, 0)
    df["rsi_mid"]        = rsi_14 - 50          # centred

    # RSI momentum (1-bar change in RSI)
    df["rsi_delta"]      = rsi_14.diff(1)
    df["rsi_delta2"]     = rsi_14.diff(3)

    # ── Additional momentum / volatility features ─────────────────────────
    # ATR-like normalised true range
    prev_close = close.shift(1)
    tr = pd.concat([
        high - low,
        (high - prev_close).abs(),
        (low  - prev_close).abs()
    ], axis=1).max(axis=1)
    atr_14 = tr.ewm(span=14, adjust=False).mean()
    df["atr_14"]  = atr_14
    df["natr_14"] = atr_14 / close          # normalised ATR

    # Rate-of-change over various horizons
    for n in [4, 8, 16]:
        df[f"roc_{n}"] = close.pct_change(n)

    # Bollinger Band width and %B (using 20-period SMA)
    sma_20   = close.rolling(20).mean()
    std_20   = close.rolling(20).std()
    bb_upper = sma_20 + 2 * std_20
    bb_lower = sma_20 - 2 * std_20
    df["bb_width"] = (bb_upper - bb_lower) / sma_20
    df["bb_pct"]   = (close - bb_lower) / (bb_upper - bb_lower).replace(0, np.nan)

    # Candle body and wick features
    df["body"]        = (close - open_) / close
    df["upper_wick"]  = (high - pd.concat([close, open_], axis=1).max(axis=1)) / close
    df["lower_wick"]  = (pd.concat([close, open_], axis=1).min(axis=1) - low)  / close

    # Volume-normalised momentum proxy: price range relative to ATR
    df["range_vs_atr"] = (high - low) / atr_14.replace(0, np.nan)

    # Lagged EMA cross signal
    df["ema_cross_lag1"] = df["ema_cross"].shift(1)
    df["ema_cross_lag2"] = df["ema_cross"].shift(2)

    # Combined signal: RSI and EMA cross alignment
    df["rsi_ema_bull"] = np.where((rsi_14 > 50) & (ema_9 > ema_21), 1, 0)
    df["rsi_ema_bear"] = np.where((rsi_14 < 50) & (ema_9 < ema_21), 1, 0)

    # Hour-of-day (cyclical encoding) — no lookahead
    hour = df.index.hour
    df["hour_sin"] = np.sin(2 * np.pi * hour / 24)
    df["hour_cos"] = np.cos(2 * np.pi * hour / 24)

    # Day-of-week (cyclical encoding)
    dow = df.index.dayofweek
    df["dow_sin"] = np.sin(2 * np.pi * dow / 5)
    df["dow_cos"] = np.cos(2 * np.pi * dow / 5)

    # Fill NaN from warm-up periods
    df = df.bfill().ffill()

    return df

# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "AUD/USD EMA Cross (9/21) + RSI14 XGBoost Scalper",
        "model_type":       "XGBClassifier",
        "model_params":     {
            "n_estimators":     400,
            "max_depth":        4,
            "learning_rate":    0.04,
            "subsample":        0.8,
            "colsample_bytree": 0.75,
            "min_child_weight": 3,
            "gamma":            0.1,
            "reg_alpha":        0.05,
            "reg_lambda":       1.5,
            "objective":        "binary:logistic",
            "tree_method":      "hist",
            "random_state":     42,
        },
        "signal_threshold": 0.54,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [0, 23],
        "min_atr":          None,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective":        (
            "Maximise risk-adjusted return on AUD/USD 15-min bars. "
            "XGBoost chosen for its ability to capture non-linear interactions between "
            "the EMA-cross regime, RSI momentum, volatility (NATR/BB width), and time-of-day. "
            "Shallow trees (max_depth=4) with strong regularisation (reg_lambda=1.5, gamma=0.1) "
            "reduce overfitting on the limited 1-year window. "
            "2:1 R:R (SL=0.5%, TP=1.0%) improves Sharpe; reverse on opposite signal captures "
            "trend momentum without missing transitions."
        ),
        "notes":            (
            "Features: EMA-9/21 cross and distances, RSI-14 with regime flags and delta, "
            "ATR-14, NATR, Bollinger Band width/%B, 4/8/16-bar ROC, candle anatomy, "
            "time cyclical encodings. Threshold 0.54 filters marginal signals to raise precision. "
            "No session filter applied — AUD/USD has meaningful moves across Asian and London sessions."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-05 10:44:36
# Model            : XGBoost
# Feature Eng.     : SMA (20,50,200) + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/EURUSD_15min.parquet"
START_DATE = "2025-04-23"
END_DATE   = "2026-04-23"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # ── SMA core features (required) ──────────────────────────────────────────
    for p in [20, 50, 200]:
        sma = close.rolling(p).mean()
        df[f"sma_{p}"]    = sma
        df[f"dm_sma_{p}"] = (close - sma) / sma

    # ── SMA cross-ratios ──────────────────────────────────────────────────────
    df["sma_20_50_ratio"]  = df["sma_20"]  / df["sma_50"]
    df["sma_50_200_ratio"] = df["sma_50"]  / df["sma_200"]
    df["sma_20_200_ratio"] = df["sma_20"]  / df["sma_200"]

    # ── SMA slope (rate of change of SMA over N bars) ─────────────────────────
    for p in [20, 50, 200]:
        df[f"sma_{p}_slope5"] = df[f"sma_{p}"].diff(5) / df[f"sma_{p}"].shift(5)

    # ── Price momentum / returns ───────────────────────────────────────────────
    for lag in [1, 2, 3, 4, 8, 16]:
        df[f"ret_{lag}"] = close.pct_change(lag)

    # ── Volatility (rolling std of returns) ───────────────────────────────────
    ret1 = close.pct_change(1)
    for w in [8, 20, 50]:
        df[f"vol_std_{w}"] = ret1.rolling(w).std()

    # ── ATR (manual) ──────────────────────────────────────────────────────────
    tr = pd.concat([
        high - low,
        (high - close.shift(1)).abs(),
        (low  - close.shift(1)).abs()
    ], axis=1).max(axis=1)
    for w in [8, 14, 20]:
        atr = tr.rolling(w).mean()
        df[f"atr_{w}"]  = atr
        df[f"natr_{w}"] = atr / close

    # ── RSI (manual) ──────────────────────────────────────────────────────────
    for period in [7, 14, 21]:
        delta = close.diff(1)
        gain  = delta.clip(lower=0).rolling(period).mean()
        loss  = (-delta.clip(upper=0)).rolling(period).mean()
        rs    = gain / loss.replace(0, np.nan)
        df[f"rsi_{period}"] = 100 - (100 / (1 + rs))

    # ── MACD (manual) ─────────────────────────────────────────────────────────
    ema12 = close.ewm(span=12, adjust=False).mean()
    ema26 = close.ewm(span=26, adjust=False).mean()
    macd_line   = ema12 - ema26
    signal_line = macd_line.ewm(span=9, adjust=False).mean()
    df["macd_line"]   = macd_line
    df["macd_signal"] = signal_line
    df["macd_hist"]   = macd_line - signal_line
    df["macd_norm"]   = macd_line / close

    # ── Bollinger Bands (manual) ──────────────────────────────────────────────
    for w in [20]:
        mid  = close.rolling(w).mean()
        std  = close.rolling(w).std()
        upper = mid + 2 * std
        lower = mid - 2 * std
        bw    = (upper - lower) / mid
        pct_b = (close - lower) / (upper - lower).replace(0, np.nan)
        df[f"bb_upper_{w}"] = upper
        df[f"bb_lower_{w}"] = lower
        df[f"bb_width_{w}"] = bw
        df[f"bb_pct_{w}"]   = pct_b

    # ── Stochastic oscillator (manual) ────────────────────────────────────────
    for k_period in [14]:
        lo_k = low.rolling(k_period).min()
        hi_k = high.rolling(k_period).max()
        stoch_k = 100 * (close - lo_k) / (hi_k - lo_k).replace(0, np.nan)
        stoch_d = stoch_k.rolling(3).mean()
        df[f"stoch_k_{k_period}"] = stoch_k
        df[f"stoch_d_{k_period}"] = stoch_d

    # ── CCI (manual) ──────────────────────────────────────────────────────────
    for w in [14, 20]:
        tp     = (high + low + close) / 3
        tp_ma  = tp.rolling(w).mean()
        tp_md  = tp.rolling(w).apply(lambda x: np.mean(np.abs(x - x.mean())), raw=True)
        df[f"cci_{w}"] = (tp - tp_ma) / (0.015 * tp_md.replace(0, np.nan))

    # ── Williams %R (manual) ──────────────────────────────────────────────────
    for w in [14]:
        hi_w = high.rolling(w).max()
        lo_w = low.rolling(w).min()
        df[f"willr_{w}"] = -100 * (hi_w - close) / (hi_w - lo_w).replace(0, np.nan)

    # ── Donchian channel position ──────────────────────────────────────────────
    for w in [20, 50]:
        hi_d = high.rolling(w).max()
        lo_d = low.rolling(w).min()
        df[f"donch_pos_{w}"] = (close - lo_d) / (hi_d - lo_d).replace(0, np.nan)

    # ── Rolling high / low distances ──────────────────────────────────────────
    for w in [8, 20]:
        df[f"dist_hi_{w}"] = (high.rolling(w).max() - close) / close
        df[f"dist_lo_{w}"] = (close - low.rolling(w).min())  / close

    # ── Bar body / wick features ───────────────────────────────────────────────
    body      = (close - open_).abs()
    bar_range = (high - low).replace(0, np.nan)
    df["body_ratio"]      = body / bar_range
    df["upper_wick_ratio"] = (high - close.clip(lower=open_)) / bar_range
    df["lower_wick_ratio"] = (close.clip(upper=open_) - low) / bar_range
    df["bar_direction"]    = np.where(close >= open_, 1.0, -1.0)

    # ── Trend regime flags (binary) ────────────────────────────────────────────
    df["above_sma20"]  = np.where(close > df["sma_20"],  1.0, 0.0)
    df["above_sma50"]  = np.where(close > df["sma_50"],  1.0, 0.0)
    df["above_sma200"] = np.where(close > df["sma_200"], 1.0, 0.0)
    df["sma20_above50"] = np.where(df["sma_20"] > df["sma_50"], 1.0, 0.0)
    df["sma50_above200"] = np.where(df["sma_50"] > df["sma_200"], 1.0, 0.0)

    # ── Lagged returns for autoregressive signal ───────────────────────────────
    for lag in [1, 2, 3, 4, 5]:
        df[f"close_lag_{lag}"] = close.shift(lag)
        df[f"ret_lag_{lag}"]   = ret1.shift(lag)

    # ── Rolling correlation: price vs SMA distance ─────────────────────────────
    for w in [20]:
        df[f"autocorr_ret_{w}"] = ret1.rolling(w).apply(
            lambda x: pd.Series(x).autocorr(lag=1) if len(x) > 1 else 0.0, raw=False
        )

    # ── Volume-proxy: bar range z-score ───────────────────────────────────────
    for w in [20]:
        rng_mean = bar_range.rolling(w).mean()
        rng_std  = bar_range.rolling(w).std().replace(0, np.nan)
        df[f"range_zscore_{w}"] = (bar_range - rng_mean) / rng_std

    # ── Fill NaN from warm-up ─────────────────────────────────────────────────
    df = df.bfill().ffill()
    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "EUR/USD SMA Trend + Multi-Indicator XGBoost",
        "model_type":       "XGBClassifier",
        "model_params": {
            "n_estimators":       500,
            "max_depth":          4,
            "learning_rate":      0.03,
            "subsample":          0.8,
            "colsample_bytree":   0.7,
            "colsample_bylevel":  0.8,
            "min_child_weight":   5,
            "gamma":              0.1,
            "reg_alpha":          0.1,
            "reg_lambda":         2.0,
            "scale_pos_weight":   1,
            "objective":          "binary:logistic",
            "tree_method":        "hist",
            "random_state":       42,
            "n_jobs":             -1,
        },
        "signal_threshold": 0.56,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.01,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [6, 18],
        "min_atr":          0.0002,
        "trend_filter":     "sma_50",
        "target_horizon":   4,
        "objective":        (
            "Maximize risk-adjusted return (Sharpe/Calmar) via XGBoost with deep "
            "SMA-based trend features (20/50/200), momentum, volatility, RSI, MACD, "
            "Bollinger Bands, Stochastics, CCI, Donchian channels, and bar microstructure. "
            "Regularised tree ensemble (L1+L2, subsampling) prevents overfit on 15-min FX data. "
            "2:1 reward-to-risk with 0.5% SL / 1.0% TP targets consistent positive expectancy. "
            "Session filter [6,18] UTC focuses on liquid London+NY overlap. "
            "Trend filter sma_50 suppresses counter-trend noise."
        ),
        "notes": (
            "n_estimators=500 with low learning_rate=0.03 gives stable generalisation. "
            "max_depth=4 limits tree complexity to avoid overfit on 15-min EURUSD. "
            "min_child_weight=5 and gamma=0.1 add conservative splitting constraints. "
            "reg_lambda=2.0 strong L2 regularisation for stable leaf weights. "
            "colsample_bytree=0.7 adds feature bagging diversity. "
            "target_horizon=4 (1 hour ahead) balances signal frequency and predictability. "
            "signal_threshold=0.56 filters marginal predictions, improving precision."
        ),
    }