Community Scripts

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 03:05:53
# 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/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):
    # ── RSI 14 ──────────────────────────────────────────────────────────────
    period = 14
    delta = close.diff()
    gain = delta.clip(lower=0)
    loss = -delta.clip(upper=0)
    avg_gain = gain.ewm(com=period - 1, min_periods=period).mean()
    avg_loss = loss.ewm(com=period - 1, min_periods=period).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()
    macd_hist = macd_line - signal_line
    df["macd_line"] = macd_line
    df["macd_signal"] = signal_line
    df["macd_hist"] = macd_hist

    # ── RSI derived features ─────────────────────────────────────────────────
    df["rsi_14_lag1"] = df["rsi_14"].shift(1)
    df["rsi_14_lag2"] = df["rsi_14"].shift(2)
    df["rsi_14_delta"] = df["rsi_14"] - df["rsi_14_lag1"]
    df["rsi_ob"] = np.where(df["rsi_14"] > 70, 1, 0)
    df["rsi_os"] = np.where(df["rsi_14"] < 30, 1, 0)

    # ── MACD derived features ────────────────────────────────────────────────
    df["macd_hist_lag1"] = macd_hist.shift(1)
    df["macd_hist_delta"] = macd_hist - macd_hist.shift(1)
    df["macd_cross_bull"] = np.where((macd_line > signal_line) & (macd_line.shift(1) <= signal_line.shift(1)), 1, 0)
    df["macd_cross_bear"] = np.where((macd_line < signal_line) & (macd_line.shift(1) >= signal_line.shift(1)), 1, 0)
    df["macd_above_zero"] = np.where(macd_line > 0, 1, 0)
    df["macd_hist_positive"] = np.where(macd_hist > 0, 1, 0)

    # ── 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_14 = tr.ewm(span=14, adjust=False).mean()
    df["atr_14"] = atr_14
    df["natr_14"] = atr_14 / close

    # ── Bollinger Bands (20, 2) ───────────────────────────────────────────────
    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_b"] = (close - bb_lower) / (bb_upper - bb_lower).replace(0, np.nan)
    df["bb_width"] = (bb_upper - bb_lower) / bb_mid.replace(0, np.nan)
    df["bb_squeeze"] = np.where(df["bb_width"] < df["bb_width"].rolling(50).mean(), 1, 0)

    # ── Price momentum features ───────────────────────────────────────────────
    df["ret_1"] = close.pct_change(1)
    df["ret_4"] = close.pct_change(4)
    df["ret_8"] = close.pct_change(8)
    df["ret_16"] = close.pct_change(16)

    # ── Rolling volatility ────────────────────────────────────────────────────
    df["vol_8"] = df["ret_1"].rolling(8).std()
    df["vol_20"] = df["ret_1"].rolling(20).std()

    # ── EMA trend features ───────────────────────────────────────────────────
    ema_20 = close.ewm(span=20, adjust=False).mean()
    ema_50 = close.ewm(span=50, adjust=False).mean()
    df["ema_20"] = ema_20
    df["ema_50"] = ema_50
    df["price_vs_ema20"] = (close - ema_20) / ema_20
    df["price_vs_ema50"] = (close - ema_50) / ema_50
    df["ema20_vs_ema50"] = (ema_20 - ema_50) / ema_50

    # ── 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
    df["lower_wick"] = (pd.concat([close, open_], axis=1).min(axis=1) - low) / candle_range
    df["bull_candle"] = np.where(close > open_, 1, 0)

    # ── Rolling high/low position ─────────────────────────────────────────────
    roll_high_20 = high.rolling(20).max()
    roll_low_20 = low.rolling(20).min()
    roll_range_20 = (roll_high_20 - roll_low_20).replace(0, np.nan)
    df["price_position_20"] = (close - roll_low_20) / roll_range_20

    # ── RSI + MACD interaction ────────────────────────────────────────────────
    df["rsi_macd_product"] = df["rsi_14"] * macd_hist
    df["rsi_norm"] = (df["rsi_14"] - 50) / 50

    # ── Session / time features ───────────────────────────────────────────────
    if hasattr(close.index, "hour"):
        df["hour"] = close.index.hour
        df["session_london"] = np.where((close.index.hour >= 7) & (close.index.hour < 16), 1, 0)
        df["session_ny"] = np.where((close.index.hour >= 13) & (close.index.hour < 21), 1, 0)
        df["session_asia"] = np.where((close.index.hour >= 22) | (close.index.hour < 7), 1, 0)
    else:
        df["hour"] = 0
        df["session_london"] = 0
        df["session_ny"] = 0
        df["session_asia"] = 0

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

    return df

# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title": "AUD/USD RSI+MACD Gradient Boosting Scalper",
        "model_type": "GradientBoostingClassifier",
        "model_params": {
            "n_estimators": 400,
            "max_depth": 4,
            "learning_rate": 0.05,
            "subsample": 0.8,
            "min_samples_leaf": 20,
            "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": 0.01,
        "cooldown": 0,
        "max_positions": 1,
        "on_opposite": "reverse",
        "session_filter": None,
        "min_atr": None,
        "trend_filter": None,
        "target_horizon": 4,
        "objective": (
            "Maximize risk-adjusted return on AUD/USD 15-min data using GradientBoostingClassifier. "
            "RSI-14 captures momentum extremes and divergence conditions; MACD (12,26,9) provides "
            "trend direction and momentum shifts via crossovers and histogram slope. Additional "
            "features (BB, ATR, EMA trend, candle structure, session timing) enrich the feature "
            "space. GradientBoosting with shallow trees (depth=4), moderate learning rate (0.05), "
            "and early stopping via n_iter_no_change prevents overfitting. SL=0.5%, TP=1.0% gives "
            "a 1:2 risk/reward ratio, improving Sharpe and Calmar. Threshold=0.55 filters low-confidence "
            "signals to reduce noise. Reverse on opposite signal maximizes capital efficiency."
        ),
        "notes": (
            "n_estimators=400 with early stopping balances bias-variance. max_depth=4 keeps trees "
            "shallow to reduce overfitting on FX microstructure noise. subsample=0.8 adds stochastic "
            "gradient boosting regularization. min_samples_leaf=20 prevents fitting to outlier bars. "
            "max_features='sqrt' adds feature randomization similar to random forests. "
            "target_horizon=4 (1 hour) aligns with typical AUD/USD intraday swing durations."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-08 02:08:02
# Model            : XGBoost
# Feature Eng.     : Auto-add features: ON
# Signal / Entry   : —
# Optimization     : —
# Risk Mgmt        : —
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

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

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDJPY_1min.parquet"
START_DATE = "2026-05-04 00:00:00"
END_DATE   = "2026-05-07 00:00:00"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.6993736951983298

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # --- Returns over multiple horizons ---
    for n in [1, 3, 5, 10, 20]:
        df[f"ret_{n}"] = close.pct_change(n)

    # --- RSI 14 ---
    delta = close.diff()
    gain = delta.clip(lower=0)
    loss = -delta.clip(upper=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 + 1e-10)
    df["rsi_14"] = 100.0 - (100.0 / (1.0 + rs))

    # --- RSI derived features ---
    df["rsi_14_zscore"] = (df["rsi_14"] - df["rsi_14"].rolling(50).mean()) / (df["rsi_14"].rolling(50).std() + 1e-10)
    df["rsi_overbought"] = np.where(df["rsi_14"] > 70, 1, 0)
    df["rsi_oversold"]   = np.where(df["rsi_14"] < 30, 1, 0)

    # --- Bollinger Bands 20, 2 ---
    bb_mid   = close.rolling(20).mean()
    bb_std   = close.rolling(20).std()
    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 + 1e-10)
    df["bb_pct_b"]  = (close - bb_lower) / (bb_upper - bb_lower + 1e-10)
    df["bb_above"]  = np.where(close > bb_upper, 1, 0)
    df["bb_below"]  = np.where(close < bb_lower, 1, 0)

    # --- Moving Averages ---
    for w in [50, 100, 200]:
        df[f"sma_{w}"] = close.rolling(w).mean()
        df[f"price_vs_sma_{w}"] = (close - df[f"sma_{w}"]) / (df[f"sma_{w}"] + 1e-10)

    # --- MA crossover signals ---
    df["sma50_vs_sma100"] = np.where(df["sma_50"] > df["sma_100"], 1, -1)
    df["sma50_vs_sma200"] = np.where(df["sma_50"] > df["sma_200"], 1, -1)
    df["sma100_vs_sma200"] = np.where(df["sma_100"] > df["sma_200"], 1, -1)

    # --- ATR 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=13, min_periods=14).mean()
    df["natr_14"] = df["atr_14"] / (close + 1e-10)

    # --- Momentum / rate of change ---
    for n in [5, 10, 20]:
        df[f"mom_{n}"] = close - close.shift(n)
        df[f"roc_{n}"] = (close - close.shift(n)) / (close.shift(n) + 1e-10)

    # --- Volume features (if volume exists) ---
    if "volume" in df.columns:
        vol = df["volume"].replace(0, np.nan)
        df["vol_sma_20"]   = vol.rolling(20).mean()
        df["vol_ratio_20"] = vol / (df["vol_sma_20"] + 1e-10)
    else:
        df["vol_ratio_20"] = 1.0

    # --- Price spread & body features ---
    df["hl_spread"]  = (high - low) / (close + 1e-10)
    df["body_ratio"] = (close - open_).abs() / (high - low + 1e-10)
    df["upper_wick"] = (high - pd.concat([close, open_], axis=1).max(axis=1)) / (high - low + 1e-10)
    df["lower_wick"] = (pd.concat([close, open_], axis=1).min(axis=1) - low) / (high - low + 1e-10)
    df["bull_candle"] = np.where(close > open_, 1, 0)

    # --- Lagged returns for autocorrelation signal ---
    for lag in [1, 2, 3, 5]:
        df[f"ret1_lag{lag}"] = df["ret_1"].shift(lag)

    # --- Rolling volatility ---
    df["vol_10"]  = df["ret_1"].rolling(10).std()
    df["vol_20"]  = df["ret_1"].rolling(20).std()
    df["vol_50"]  = df["ret_1"].rolling(50).std()

    # --- Z-score of close over 20 and 50 bars ---
    df["zscore_20"] = (close - close.rolling(20).mean()) / (close.rolling(20).std() + 1e-10)
    df["zscore_50"] = (close - close.rolling(50).mean()) / (close.rolling(50).std() + 1e-10)

    # --- Relative distance of price from BB bands ---
    df["dist_upper"] = (bb_upper - close) / (close + 1e-10)
    df["dist_lower"] = (close - bb_lower) / (close + 1e-10)

    # --- EMA 9 and 21 for short-term momentum ---
    df["ema_9"]  = close.ewm(span=9, min_periods=9).mean()
    df["ema_21"] = close.ewm(span=21, min_periods=21).mean()
    df["ema9_vs_ema21"] = np.where(df["ema_9"] > df["ema_21"], 1, -1)
    df["price_vs_ema9"]  = (close - df["ema_9"]) / (df["ema_9"] + 1e-10)
    df["price_vs_ema21"] = (close - df["ema_21"]) / (df["ema_21"] + 1e-10)

    # --- MACD-like signal ---
    ema_12 = close.ewm(span=12, min_periods=12).mean()
    ema_26 = close.ewm(span=26, min_periods=26).mean()
    macd_line   = ema_12 - ema_26
    signal_line = macd_line.ewm(span=9, min_periods=9).mean()
    df["macd"]         = macd_line
    df["macd_signal"]  = signal_line
    df["macd_hist"]    = macd_line - signal_line
    df["macd_cross"]   = np.where(macd_line > signal_line, 1, -1)

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

    return df

# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "USD/JPY Multi-MA + RSI/BB XGBoost Sharpe",
        "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": 5,
            "gamma":            0.1,
            "reg_alpha":        0.1,
            "reg_lambda":       1.5,
            "objective":        "binary:logistic",
            "tree_method":      "hist",
            "random_state":     42,
        },
        "signal_threshold": 0.55,
        "direction":        "both",
        "stop_loss":        0.0008,
        "take_profit":      0.0016,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "close_only",
        "session_filter":   None,
        "min_atr":          None,
        "trend_filter":     None,
        "target_horizon":   5,
        "objective":        (
            "Maximize Sharpe ratio on USD/JPY 1-min data using XGBoost with "
            "returns, RSI, Bollinger Bands, multiple MAs (50/100/200), MACD, "
            "ATR, and candle-body features. Stop-loss and take-profit set at "
            "a 1:2 risk/reward to filter noise and improve Sharpe. n_estimators "
            "and moderate depth balance bias-variance. Regularization (alpha/lambda) "
            "reduces overfitting on short date range."
        ),
        "notes":            (
            "Target horizon of 5 bars (5 minutes) is chosen to capture short-term "
            "directional moves on 1-min data without excessive label noise. "
            "colsample_bytree and subsample add stochasticity to reduce variance. "
            "close_only on opposite signal avoids whipsaw from rapid reversals. "
            "No session filter applied since USD/JPY has liquidity around the clock."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-24 01:27:47
# Model            : XGBoost
# Feature Eng.     : deploy a 15min EURUSD model + Auto-add features: ON
# Signal / Entry   : —
# Optimization     : —
# Risk Mgmt        : —
# 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 = "2026-04-14"
END_DATE   = "2026-05-12"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # ── Price returns ──────────────────────────────────────────────────────
    df["ret_1"]  = close.pct_change(1)
    df["ret_2"]  = close.pct_change(2)
    df["ret_4"]  = close.pct_change(4)
    df["ret_8"]  = close.pct_change(8)
    df["ret_16"] = close.pct_change(16)
    df["ret_32"] = close.pct_change(32)

    # ── Candle body / wick features ────────────────────────────────────────
    hl   = (high - low).replace(0, np.nan)
    body = (close - open_).abs()
    df["body_ratio"]      = body / hl
    df["upper_wick"]      = (high - np.maximum(close, open_)) / hl
    df["lower_wick"]      = (np.minimum(close, open_) - low)  / hl
    df["candle_dir"]      = np.sign(close - open_)

    # ── RSI (14) ───────────────────────────────────────────────────────────
    def _rsi(src, n=14):
        delta = src.diff()
        gain  = delta.clip(lower=0)
        loss  = (-delta).clip(lower=0)
        avg_g = gain.ewm(com=n - 1, min_periods=n).mean()
        avg_l = loss.ewm(com=n - 1, min_periods=n).mean()
        rs    = avg_g / avg_l.replace(0, np.nan)
        return 100 - (100 / (1 + rs))

    df["rsi_14"] = _rsi(close, 14)
    df["rsi_7"]  = _rsi(close,  7)
    df["rsi_21"] = _rsi(close, 21)

    # RSI normalised distance from 50
    df["rsi_14_dev"] = (df["rsi_14"] - 50) / 50

    # ── EMA crossovers ─────────────────────────────────────────────────────
    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()
    ema200 = close.ewm(span=200, adjust=False).mean()

    df["ema8"]       = ema8
    df["ema21"]      = ema21
    df["ema50"]      = ema50

    df["ema8_21_xo"]   = (ema8  - ema21)  / close
    df["ema21_50_xo"]  = (ema21 - ema50)  / close
    df["ema50_200_xo"] = (ema50 - ema200) / close

    # Price distance from EMAs (normalised)
    df["dist_ema8"]   = (close - ema8)   / close
    df["dist_ema21"]  = (close - ema21)  / close
    df["dist_ema50"]  = (close - ema50)  / close
    df["dist_ema200"] = (close - ema200) / close

    # ── MACD ───────────────────────────────────────────────────────────────
    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_line"]   = macd_line   / close
    df["macd_signal"] = macd_signal / close
    df["macd_hist"]   = (macd_line - macd_signal) / close
    df["macd_hist_chg"] = df["macd_hist"].diff()

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

    # ── 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(com=13, min_periods=14).mean()
    natr  = atr14 / close
    df["atr14"]  = atr14
    df["natr14"] = natr
    df["natr14_chg"] = natr.diff()

    # ── Momentum / Rate of Change ──────────────────────────────────────────
    df["mom_4"]  = close - close.shift(4)
    df["mom_8"]  = close - close.shift(8)
    df["roc_10"] = (close / close.shift(10).replace(0, np.nan)) - 1
    df["roc_20"] = (close / close.shift(20).replace(0, np.nan)) - 1

    # ── Stochastic Oscillator (14,3) ───────────────────────────────────────
    low14  = low.rolling(14).min()
    high14 = high.rolling(14).max()
    stoch_k = (close - low14) / (high14 - low14).replace(0, np.nan) * 100
    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 (if available, else dummy) ──────────────────────────────────
    if "volume" in df.columns:
        vol = df["volume"].replace(0, np.nan)
        vol_ma20   = vol.rolling(20).mean()
        df["vol_ratio"]  = vol / vol_ma20
        df["vol_chg"]    = vol.pct_change()
    else:
        df["vol_ratio"] = 1.0
        df["vol_chg"]   = 0.0

    # ── Rolling volatility ─────────────────────────────────────────────────
    df["realvol_8"]  = df["ret_1"].rolling(8).std()
    df["realvol_20"] = df["ret_1"].rolling(20).std()
    df["vol_ratio_short_long"] = df["realvol_8"] / df["realvol_20"].replace(0, np.nan)

    # ── Highs/Lows distance (support/resistance proxy) ─────────────────────
    df["high_20_dist"] = (high.rolling(20).max() - close) / close
    df["low_20_dist"]  = (close - low.rolling(20).min())  / close
    df["high_5_dist"]  = (high.rolling(5).max()  - close) / close
    df["low_5_dist"]   = (close - low.rolling(5).min())   / close

    # ── Lagged features ────────────────────────────────────────────────────
    for col in ["rsi_14_dev", "macd_hist", "bb_pct", "natr14", "stoch_kd"]:
        df[f"{col}_lag1"] = df[col].shift(1)
        df[f"{col}_lag2"] = df[col].shift(2)
        df[f"{col}_lag4"] = df[col].shift(4)

    # ── Session dummies (hour-of-day in UTC) ───────────────────────────────
    hour = close.index.hour
    df["session_london"]   = np.where((hour >= 7)  & (hour < 16), 1, 0)
    df["session_ny"]       = np.where((hour >= 13) & (hour < 21), 1, 0)
    df["session_overlap"]  = np.where((hour >= 13) & (hour < 16), 1, 0)
    df["session_asia"]     = np.where((hour >= 0)  & (hour < 7),  1, 0)

    # Day-of-week
    dow = close.index.dayofweek
    df["dow_mon"] = np.where(dow == 0, 1, 0)
    df["dow_fri"] = np.where(dow == 4, 1, 0)

    # ── Interaction features ───────────────────────────────────────────────
    df["rsi_macd"]   = df["rsi_14_dev"] * df["macd_hist"]
    df["rsi_bbpct"]  = df["rsi_14_dev"] * df["bb_pct"]
    df["macd_vol"]   = df["macd_hist"]  * df["vol_ratio"]
    df["natr_bbwid"] = df["natr14"]     * df["bb_width"]

    # ── SMA50 for trend filter ─────────────────────────────────────────────
    df["sma_50"] = close.rolling(50).mean()

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

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title": "EUR/USD XGBoost Multi-Feature Sharpe Maximiser",

        "model_type": "XGBClassifier",

        "model_params": {
            "n_estimators":       400,
            "max_depth":          4,
            "learning_rate":      0.03,
            "subsample":          0.75,
            "colsample_bytree":   0.70,
            "colsample_bylevel":  0.80,
            "min_child_weight":   5,
            "gamma":              0.1,
            "reg_alpha":          0.05,
            "reg_lambda":         1.5,
            "scale_pos_weight":   1.0,
            "objective":          "binary:logistic",
            "tree_method":        "hist",
            "random_state":       42,
            "n_jobs":             -1,
        },

        "signal_threshold": 0.55,

        "direction":     "both",
        "max_positions": 1,
        "on_opposite":   "reverse",
        "cooldown":      0,

        "stop_loss":   0.0008,
        "take_profit": 0.0016,

        "session_filter": [7, 21],

        "min_atr":      0.00015,
        "trend_filter": None,

        "target_horizon": 4,

        "objective": (
            "Maximise Sharpe ratio on 15-min EUR/USD. "
            "XGBoost with moderate depth (4) and heavy regularisation "
            "(reg_lambda=1.5, min_child_weight=5, gamma=0.1) controls overfitting "
            "on the short date window. Low learning_rate (0.03) with 400 trees "
            "for stable convergence. SL=0.8 pip / TP=1.6 pip gives 1:2 RR to "
            "preserve Sharpe. Session filter [7,21] removes illiquid Asia opens. "
            "min_atr filters flat, low-volatility bars that degrade signal quality."
        ),

        "notes": (
            "Feature set covers trend (EMA crosses, MACD), mean-reversion (RSI, BB%B), "
            "volatility (ATR, realvol), momentum (ROC, Stochastic), microstructure "
            "(candle body/wick ratios), session dummies, and lagged versions of key "
            "signals to give the model temporal context without lookahead. "
            "Interaction terms (rsi*macd, rsi*bb_pct) capture combined regime signals. "
            "target_horizon=4 bars (1 hour) balances enough price movement to overcome "
            "2e-5 round-trip cost while avoiding excessive label noise."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 01:50:17
# 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/USDCAD_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_s  = close.rolling(bb_period).std(ddof=0)
    bb_upper  = bb_mid + bb_std * bb_std_s
    bb_lower  = bb_mid - bb_std * bb_std_s

    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)

    # ── ATR (14) & Normalised ATR ────────────────────────────────────────────
    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, adjust=False).mean()
    natr = atr / close

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

    # ── 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, adjust=False).mean()
    avg_loss   = loss.ewm(span=rsi_period, adjust=False).mean()
    rs         = avg_gain / avg_loss.replace(0, np.nan)
    df["rsi"]  = 100 - (100 / (1 + rs))

    # ── 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()
    df["macd"]     = macd_line
    df["macd_sig"] = macd_signal
    df["macd_hist"]= macd_line - macd_signal

    # ── SMA filters (50, 200) ────────────────────────────────────────────────
    df["sma_20"]  = close.rolling(20).mean()
    df["sma_50"]  = close.rolling(50).mean()
    df["sma_200"] = close.rolling(200).mean()

    # Price relative to moving averages
    df["close_vs_sma20"]  = (close - df["sma_20"])  / df["sma_20"]
    df["close_vs_sma50"]  = (close - df["sma_50"])  / df["sma_50"]
    df["close_vs_sma200"] = (close - df["sma_200"]) / df["sma_200"]

    # ── Price momentum / returns ─────────────────────────────────────────────
    df["ret_1"]  = close.pct_change(1)
    df["ret_4"]  = close.pct_change(4)
    df["ret_8"]  = close.pct_change(8)
    df["ret_16"] = close.pct_change(16)
    df["ret_32"] = close.pct_change(32)

    # ── 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
    df["lower_wick"]  = (pd.concat([close, open_], axis=1).min(axis=1) - low) / candle_range
    df["close_dir"]   = np.sign(close - open_)

    # ── Volatility regime ────────────────────────────────────────────────────
    df["vol_ratio"]  = natr / natr.rolling(50).mean()         # ATR vs its own average
    df["bb_squeeze"] = np.where(df["bb_width"] < df["bb_width"].rolling(50).quantile(0.25), 1.0, 0.0)

    # ── 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

    # ── Rate-of-change ───────────────────────────────────────────────────────
    df["roc_10"] = (close - close.shift(10)) / close.shift(10)

    # ── Rolling z-score of close (20-bar) ────────────────────────────────────
    roll_mean = close.rolling(20).mean()
    roll_std  = close.rolling(20).std(ddof=0).replace(0, np.nan)
    df["zscore_20"] = (close - roll_mean) / roll_std

    # ── Volume-related (if volume column exists) ─────────────────────────────
    if "volume" in df.columns and df["volume"].sum() > 0:
        vol_ma          = df["volume"].rolling(20).mean().replace(0, np.nan)
        df["vol_ratio_20"] = df["volume"] / vol_ma

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

    return df

# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title": "USD/CAD BB + ATR Gradient Boosting Mean-Rev",
        "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":   [7, 20],
        "min_atr":          0.0002,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective": (
            "Maximize risk-adjusted return (Sharpe/Calmar) on USD/CAD 15-min data. "
            "GradientBoostingClassifier chosen for strong generalisation on noisy FX "
            "price data; moderate depth (4) and learning rate (0.04) with early stopping "
            "prevent overfitting. Features: Bollinger Bands (mean-reversion signal via "
            "bb_pct and bb_width), ATR/NATR (volatility filter), RSI, MACD, Stochastic, "
            "z-score, momentum returns, and candle-body ratios. 2:1 R:R (SL 0.5%, TP 1.0%) "
            "with session filter (07-20 UTC) to avoid illiquid overnight hours."
        ),
        "notes": (
            "session_filter [7,20] captures London + NY overlap on USD/CAD. "
            "min_atr 0.0002 avoids flat/choppy markets. on_opposite=reverse ensures "
            "the model flips direction quickly when sentiment changes. "
            "target_horizon=4 bars (1 hour) aligns with typical intraday FX moves."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  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 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        : 2026-05-05 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."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-25 02:26:36
# Model            : XGBoost
# Feature Eng.     : buy when RSI(14) crosses up from below 30, sell when it crosses down from above 70 + Auto-add features: ON
# Signal / Entry   : —
# Optimization     : —
# Risk Mgmt        : —
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

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

# ── Inlined strategy_utils ──
"""
strategy_utils.py — Standard utility functions for generated strategies.
Claude imports these instead of writing boilerplate from scratch.
This ensures consistent behavior across all generated strategies.
"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder


# Max backtest window per timeframe. A finer timeframe over a longer window
# blows up the results dict / parquet load / Modal train time (the 2026-05-12
# OOM was a 1-min × multi-year sweep) — and a 1-min strategy gains nothing from
# 2 years of 1-min bars. Enforced HERE because every training path (UI / API /
# Modal) funnels through run_strategy → load_ohlc. Env-overridable so a future
# "max plan" / dedicated-server tier can lift it.
_TF_MAX_DAYS = {
    "1min":  30,
    "5min":  90,
    "15min": 365,
    "1h":    730,
}


def _fetch_ohlc_from_internal(symbol: str, tf: str, start: str, end: str):
    """Phase 3.2: fetch parquet bytes from Server A's /internal/ohlc endpoint
    instead of reading a local file. Used inside Modal containers / Mac worker
    pool (Phase 3.4) so every train sees the same source of truth as the chart.

    Returns: pd.DataFrame (parquet decoded), or raises on any failure so the
    caller can fall back / surface a clear error in the job.
    """
    import hashlib as _hashlib, hmac as _hmac, io as _io, os as _os
    import urllib.request as _ur, urllib.parse as _urp

    base = (_os.environ.get("QM_INTERNAL_OHLC_BASE") or "").rstrip("/")
    secret = (_os.environ.get("INTERNAL_WS_SECRET") or "").strip()
    if not base:
        raise RuntimeError("QM_INTERNAL_OHLC_BASE not set")
    if not secret:
        raise RuntimeError("INTERNAL_WS_SECRET not set")

    msg = f"{symbol}|{tf}|{start}|{end}".encode("utf-8")
    sig = _hmac.new(secret.encode("utf-8"), msg, _hashlib.sha256).hexdigest()

    qs = _urp.urlencode({
        "symbol": symbol, "tf": tf,
        "start": start, "end": end, "sig": sig,
    })
    url = f"{base}/internal/ohlc?{qs}"

    req = _ur.Request(url, headers={"User-Agent": "qm-worker/1.0"})
    with _ur.urlopen(req, timeout=30) as resp:
        if resp.status != 200:
            raise RuntimeError(f"/internal/ohlc returned {resp.status}")
        payload = resp.read()
    print(f"[load_ohlc:internal] {symbol} {tf} fetched {len(payload)} bytes", flush=True)
    return pd.read_parquet(_io.BytesIO(payload))


def _parse_symbol_tf_from_path(data_path: str):
    """Pull SYMBOL + TF out of a path like .../EURUSD_1min.parquet."""
    import os as _os, re as _re
    base = _os.path.basename(str(data_path))
    m = _re.match(r"^([A-Z]{6})_(\d+min|\d+h)\.parquet$", base)
    if not m:
        return None, None
    return m.group(1), m.group(2)


def load_ohlc(data_path, start_date="", end_date=""):
    """Load OHLC parquet, sort index, filter dates. Always returns consistent format.

    The lower bound is clamped per timeframe (see _TF_MAX_DAYS) — a request for
    more history than the cap silently starts later.

    Phase 3.2: when env QM_USE_INTERNAL_OHLC=="1", fetch over HTTP from
    Server A's /internal/ohlc endpoint instead of pd.read_parquet on a local
    file (which on Modal is a stale Volume snapshot). The endpoint applies the
    same day-cap, so the local cap-check below is a defensive no-op in that
    path. Flag defaults to "0" → unchanged behavior.

    Returns: (df, close, open_, high, low)
    """
    import os as _os, re as _re

    _use_internal = _os.environ.get("QM_USE_INTERNAL_OHLC", "0") == "1"
    if _use_internal:
        _sym, _tf = _parse_symbol_tf_from_path(data_path)
        if not _sym or not _tf:
            raise RuntimeError(
                f"QM_USE_INTERNAL_OHLC=1 but DATA_PATH basename does not match "
                f"SYMBOL_TF.parquet: {data_path}"
            )
        df = _fetch_ohlc_from_internal(_sym, _tf, start_date or "", end_date or "")
    else:
        df = pd.read_parquet(data_path)
    df.index = pd.to_datetime(df.index)
    df = df.sort_index()

    # Per-timeframe window cap (timeframe inferred from the parquet filename).
    _m = _re.search(r"_(\d+min|\d+h)\.parquet$", _os.path.basename(str(data_path)))
    _tf = _m.group(1) if _m else None
    _max_days = _TF_MAX_DAYS.get(_tf)
    if _max_days and _max_days > 0 and len(df):
        _env_override = _os.environ.get(f"QM_MAX_DAYS_{_tf.upper()}")
        if _env_override and _env_override.isdigit():
            _max_days = int(_env_override)
        try:
            _eff_end = pd.Timestamp(end_date) if end_date else df.index.max()
            _eff_end = min(_eff_end, df.index.max())
            _floor = _eff_end - pd.Timedelta(days=_max_days)
            _req_start = pd.Timestamp(start_date) if start_date else df.index.min()
            if _req_start < _floor:
                print(f"[load_ohlc] {_tf} backtest window capped to {_max_days}d: "
                      f"start {_req_start.date()} -> {_floor.date()}", flush=True)
                start_date = _floor
        except Exception as _e:
            print(f"[load_ohlc] window-cap check skipped ({_e})", flush=True)

    if start_date:
        df = df[df.index >= start_date]
    if end_date:
        df = df[df.index <= end_date]

    return df, df["close"], df["open"], df["high"], df["low"]


def make_target(close, horizon=4):
    """Create target: direction N bars ahead. Default 4 bars = 1 hour on 15-min data.

    Returns: target (pd.Series of -1, 0, 1)
    """
    return np.sign(close.shift(-horizon) - close)


def split_data(df, target, feature_cols, train_split=0.7, validation_date=""):
    """Train/test split. Handles both ratio and date-based splits.
    Drops NaN from target before splitting. Encodes labels to [0,1,2].

    Returns: dict with keys:
        X_train, X_test, y_train, y_test,
        y_train_enc, y_test_enc, enc,
        close_train, close_test,
        split_idx, split_dt, n_train, n_test
    """
    # Drop NaN from target
    mask = target.notna()
    df = df[mask].copy()
    target = target[mask]

    close = df["close"]

    # Build feature matrix
    X = df[feature_cols].copy()
    X = X.bfill().ffill()
    X = X.replace([np.inf, -np.inf], np.nan).fillna(0.0)

    # Split
    if validation_date:
        split_idx = len(df[df.index <= validation_date])
    else:
        split_idx = int(len(df) * train_split)

    split_idx = max(1, min(split_idx, len(df) - 1))

    X_train = X.iloc[:split_idx]
    X_test = X.iloc[split_idx:]
    y_train = target.iloc[:split_idx]
    y_test = target.iloc[split_idx:]
    close_train = close.iloc[:split_idx]
    close_test = close.iloc[split_idx:]

    split_dt = str(df.index[split_idx])

    # Label encoding — always fit on [-1, 0, 1]
    enc = LabelEncoder()
    enc.fit([-1, 0, 1])
    y_train_enc = enc.transform(y_train)
    y_test_enc = enc.transform(y_test)

    return {
        "df": df, "X_train": X_train, "X_test": X_test,
        "y_train": y_train, "y_test": y_test,
        "y_train_enc": y_train_enc, "y_test_enc": y_test_enc,
        "enc": enc,
        "close": close, "close_train": close_train, "close_test": close_test,
        "split_idx": split_idx, "split_dt": split_dt,
        "n_train": len(X_train), "n_test": len(X_test),
    }


def compute_overlays(close, df_index):
    """Compute BB and MA overlays on full dataset. Always consistent.

    Returns: (bb_dict, ma_dict)
    """
    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

    ma50 = close.rolling(50).mean()
    ma100 = close.rolling(100).mean()
    ma200 = close.rolling(200).mean()

    def _safe(s):
        s = s.reindex(df_index).bfill().ffill()
        return [float(x) if (x is not None and not np.isnan(x) and not np.isinf(x)) else None
                for x in s.values]

    bb = {"upper": _safe(bb_upper), "mid": _safe(bb_mid), "lower": _safe(bb_lower)}
    ma = {"ma50": _safe(ma50), "ma100": _safe(ma100), "ma200": _safe(ma200)}

    return bb, ma


def run_backtest(signal, close, capital=10000, cost=2e-5):
    """Run backtest with transaction costs.
    Uses price-based trade returns (same as webapp _compute_trades).
    Signal 0 = hold (keep current position), not close.

    Returns: dict with equity, trade_returns, long_returns, short_returns, bar_returns
    """
    sig_arr = signal.values
    price_arr = close.values
    idx = signal.index
    n = len(price_arr)

    # Trade returns — price-based (matches webapp _compute_trades exactly)
    trade_returns = []
    long_returns = []
    short_returns = []
    trade_log = []
    last_dir = None
    entry_price = None
    entry_bar = None

    for i in range(n):
        s = sig_arr[i]
        c = price_arr[i]
        if s != 0.0 and s != last_dir:
            # Direction change — close previous trade, open new
            if last_dir is not None and entry_price is not None and entry_price != 0:
                ret = float(last_dir * (c - entry_price) / entry_price - cost)
                trade_returns.append(ret)
                if last_dir == 1:
                    long_returns.append(ret)
                else:
                    short_returns.append(ret)
                trade_log.append({
                    "type": "Buy" if last_dir == 1 else "Sell",
                    "entry_time": str(idx[entry_bar]),
                    "exit_time": str(idx[i]),
                    "entry_price": round(entry_price, 5),
                    "exit_price": round(c, 5),
                    "pnl": round(last_dir * (c - entry_price), 5),
                    "pnl_pct": round(ret * 100, 3),
                    "exit_reason": "signal",
                })
            entry_price = c
            entry_bar = i
            last_dir = s

    # Close last open trade
    if last_dir is not None and entry_price is not None and n > 0 and entry_price != 0:
        c = price_arr[-1]
        ret = float(last_dir * (c - entry_price) / entry_price - cost)
        trade_returns.append(ret)
        if last_dir == 1:
            long_returns.append(ret)
        else:
            short_returns.append(ret)
        trade_log.append({
            "type": "Buy" if last_dir == 1 else "Sell",
            "entry_time": str(idx[entry_bar]),
            "exit_time": str(idx[-1]),
            "entry_price": round(entry_price, 5),
            "exit_price": round(c, 5),
            "pnl": round(last_dir * (c - entry_price), 5),
            "pnl_pct": round(ret * 100, 3),
            "exit_reason": "end",
        })

    # Equity curve from trade returns
    cumret = 1.0
    equity_vals = np.full(n, float(capital))
    trade_idx = 0
    in_trade = False
    t_entry_price = None
    t_dir = None

    for i in range(n):
        s = sig_arr[i]
        c = price_arr[i]
        if s != 0.0 and s != t_dir:
            if t_dir is not None and t_entry_price is not None and t_entry_price != 0:
                t_ret = t_dir * (c - t_entry_price) / t_entry_price - cost
                cumret *= (1 + t_ret)
            t_entry_price = c
            t_dir = s
        equity_vals[i] = capital * cumret

    # Bar returns for Sharpe
    bar_returns = np.zeros(n)
    for i in range(1, n):
        if price_arr[i - 1] != 0 and last_dir is not None:
            bar_returns[i] = sig_arr[i - 1] * (price_arr[i] - price_arr[i - 1]) / price_arr[i - 1] if sig_arr[i - 1] != 0 else 0.0

    return {
        "equity": pd.Series(equity_vals, index=close.index),
        "trade_returns": trade_returns,
        "long_returns": long_returns,
        "short_returns": short_returns,
        "bar_returns": bar_returns,
        "trade_log": trade_log,
    }


def compute_trade_stats(trades, capital=10000):
    """Single source of truth for trade statistics.
    Every display path reads from this — no recomputation anywhere.
    All values are rounded and JSON-safe (no inf/nan).
    """
    if not trades:
        return {"n": 0, "wins": 0, "losses": 0, "wr": 0, "avg": 0,
                "best": 0, "worst": 0, "ret": 0, "np": 0, "mdd": 0,
                "pf": 0, "rr": 0, "expect": 0}
    w = [r for r in trades if r > 0]
    l = [r for r in trades if r < 0]
    cumret = 1.0
    for r in trades:
        cumret *= (1 + r)
    net_p = capital * (cumret - 1)
    # Max drawdown
    eq = np.cumprod([1.0] + [1 + r for r in trades])
    peak = np.maximum.accumulate(eq)
    mdd = float(((eq - peak) / peak).min()) if len(eq) > 1 else 0.0
    # Profit Factor
    gross_w = sum(w) if w else 0
    gross_l = abs(sum(l)) if l else 0
    pf = gross_w / gross_l if gross_l > 0 else (9999.0 if gross_w > 0 else 0)
    # Risk:Reward
    avg_w = float(np.mean(w)) if w else 0
    avg_l = abs(float(np.mean(l))) if l else 0
    rr = avg_w / avg_l if avg_l > 0 else (9999.0 if avg_w > 0 else 0)
    # Expectancy
    expect = net_p / len(trades)
    return {
        "n": len(trades), "wins": len(w), "losses": len(l),
        "wr": round(len(w) / len(trades), 4),
        "avg": round(float(np.mean(trades)), 6),
        "best": round(max(w), 6) if w else 0,
        "worst": round(min(l), 6) if l else 0,
        "ret": round(cumret - 1, 6),
        "np": round(net_p, 2),
        "mdd": round(mdd, 6),
        "pf": round(pf, 2),
        "rr": round(rr, 2),
        "expect": round(expect, 2),
    }


def compute_metrics(bt_result, close_test, capital=10000):
    """Compute all standard metrics from backtest result.
    Uses trade-level compounding (same as webapp _trade_stats) for accuracy.

    Returns: dict with total_ret, bh_ret, sharpe_strat, sharpe_bh, mdd, n_trades
    """
    equity = bt_result["equity"]
    trade_returns = bt_result["trade_returns"]

    # Total return — trade-level compounding (matches webapp)
    if trade_returns:
        cumret = 1.0
        for r in trade_returns:
            cumret *= (1 + r)
        total_ret = cumret - 1
    else:
        total_ret = 0.0

    # Buy and hold
    bh_equity = capital * (close_test / close_test.iloc[0])
    bh_ret = (bh_equity.iloc[-1] - capital) / capital if capital != 0 else 0.0

    # Sharpe ratio — trade-level (matches webapp: sqrt(252*26) annualization)
    if len(trade_returns) >= 2 and float(np.std(trade_returns)) > 0:
        sharpe_strat = float(np.mean(trade_returns) / np.std(trade_returns) * np.sqrt(252 * 26))
    else:
        sharpe_strat = 0.0

    bh_rets = bh_equity.pct_change().dropna()
    if len(bh_rets) > 1 and bh_rets.std() != 0:
        sharpe_bh = float((bh_rets.mean() / bh_rets.std()) * np.sqrt(252 * 24 * 4))
    else:
        sharpe_bh = 0.0

    # Max drawdown — trade-level (matches webapp)
    if trade_returns:
        eq = np.cumprod([1.0] + [1 + r for r in trade_returns])
        peak = np.maximum.accumulate(eq)
        mdd = float(((eq - peak) / peak).min()) if len(eq) > 1 else 0.0
    else:
        mdd = 0.0

    return {
        "total_ret": float(total_ret),
        "bh_ret": float(bh_ret),
        "sharpe_strat": float(sharpe_strat) if not np.isnan(sharpe_strat) else 0.0,
        "sharpe_bh": float(sharpe_bh) if not np.isnan(sharpe_bh) else 0.0,
        "mdd": float(mdd),
        "n_trades": len(trade_returns),
    }


# Diagnostics line/histogram series (equity / drawdown / rolling_acc / conf_hist)
# only feed the small Diagnostics charts — they're never used by the price chart
# or scroll-back. On a 1-min model trained over the (2.2-capped) window these are
# still ~30k points each; downsample to a visually-identical resolution before the
# dict leaves the trainer so it doesn't carry that into Server-A RAM / Postgres.
_RESULTS_SERIES_MAX = 5000


def _downsample_idx(n, cap=_RESULTS_SERIES_MAX):
    """Evenly-spaced index list spanning [0, n-1] (first+last always kept), or
    None when no downsampling is needed (n <= cap)."""
    if n <= cap:
        return None
    return np.unique(np.linspace(0, n - 1, cap).astype(int)).tolist()


def _take(arr, idx):
    """Subset a list by an index list (idx may be None → return arr unchanged)."""
    if idx is None or not isinstance(arr, list):
        return arr
    return [arr[i] for i in idx]


# trade_log / train_trade_log are lists of per-trade dicts (display-only — the
# Trade Log tab). They scale with TRADE count, not bar count, so the bar-window
# cap (Phase 2.2) doesn't bound them — a degenerate near-every-bar model can put
# 10k+ trade dicts in the blob (>3 MB). Cap each (independently — a small-N model
# keeps every trade) to the most-recent N, recording `*_total` + `*_truncated`
# so the true count is still reported. Real strategies have far fewer than
# _TRADE_LOG_MAX trades, so this only ever bites pathological models.
_TRADE_LOG_MAX = 5000


def _cap_trade_log(tl):
    """Return (capped_list, original_len, was_truncated)."""
    if not isinstance(tl, list) or len(tl) <= _TRADE_LOG_MAX:
        return tl, (len(tl) if isinstance(tl, list) else 0), False
    return tl[-_TRADE_LOG_MAX:], len(tl), True


def build_return_dict(split_result, bt_result, metrics, model, feature_cols,
                      signal_full, p_pos_test, p_neg_test, custom_figs=None,
                      bt_train_result=None, pre_stats=None):
    """Assemble the complete return dict. Handles ALL serialization.
    Never returns Timestamps, numpy arrays, or non-JSON types.

    Returns: JSON-safe dict with all required keys
    """
    df = split_result["df"]
    close = split_result["close"]
    close_test = split_result["close_test"]
    X_test = split_result["X_test"]
    y_test = split_result["y_test"]
    equity = bt_result["equity"]
    bar_returns = bt_result["bar_returns"]

    # OHLC
    ohlc_dates = [str(x) for x in df.index.tolist()]

    def _safe_list(arr):
        return [float(x) if (x is not None and not np.isnan(x) and not np.isinf(x)) else None
                for x in arr]

    # Overlays
    bb, ma = compute_overlays(close, df.index)

    # Buy and hold equity
    capital = equity.iloc[0] if len(equity) > 0 else 10000
    bh_equity = capital * (close_test / close_test.iloc[0])

    # Confusion matrix
    from sklearn.metrics import confusion_matrix
    pred_test = model.predict(X_test)
    y_test_arr = np.asarray(y_test)
    cm = confusion_matrix(y_test_arr, pred_test, labels=[-1, 0, 1])

    # Rolling accuracy
    sig_arr = signal_full.reindex(close_test.index).values
    correct = pd.Series((pred_test == y_test_arr).astype(float), index=X_test.index)
    active_test = pd.Series(sig_arr != 0, index=close_test.index) if len(sig_arr) == len(close_test) else pd.Series(True, index=close_test.index)
    correct_active = correct.where(active_test, other=np.nan)
    rolling_acc = correct_active.rolling(30, min_periods=1).mean()

    # Feature importance
    importances = model.feature_importances_
    fi_pairs = sorted(zip(feature_cols, importances), key=lambda x: x[1])[-15:]

    # Drawdown
    rolling_max = equity.cummax()
    drawdown = (equity - rolling_max) / rolling_max.replace(0, np.nan)
    drawdown = drawdown.fillna(0.0)

    # ── Downsample the Diagnostics-only series (see _downsample_idx) ──────────
    _eq_dates = [str(x) for x in close_test.index.tolist()]
    _eq_strat = _safe_list(equity.values)
    _eq_bh    = _safe_list(bh_equity.values)
    _eq_idx   = _downsample_idx(len(_eq_dates))
    _eq_dates, _eq_strat, _eq_bh = _take(_eq_dates, _eq_idx), _take(_eq_strat, _eq_idx), _take(_eq_bh, _eq_idx)

    _ra_dates = [str(x) for x in rolling_acc.index.tolist()]
    _ra_vals  = [float(x) if (not np.isnan(x) and not np.isinf(x)) else None for x in rolling_acc.values]
    _ra_idx   = _downsample_idx(len(_ra_dates))
    _ra_dates, _ra_vals = _take(_ra_dates, _ra_idx), _take(_ra_vals, _ra_idx)

    _dd_dates = [str(x) for x in drawdown.index.tolist()]
    _dd_vals  = _safe_list(drawdown.values)
    _dd_idx   = _downsample_idx(len(_dd_dates))
    _dd_dates, _dd_vals = _take(_dd_dates, _dd_idx), _take(_dd_vals, _dd_idx)

    _cp_pos = [float(x) for x in (p_pos_test.tolist() if hasattr(p_pos_test, 'tolist') else list(p_pos_test))]
    _cp_neg = [float(x) for x in (p_neg_test.tolist() if hasattr(p_neg_test, 'tolist') else list(p_neg_test))]
    _cp_pos = _take(_cp_pos, _downsample_idx(len(_cp_pos)))
    _cp_neg = _take(_cp_neg, _downsample_idx(len(_cp_neg)))

    # ── Trade logs — display-only (Trade Log tab); cap to most-recent N with a
    #    `_total` field so the true count is still reported (see _cap_trade_log).
    #    NB: ret_dist arrays are left FULL — a downstream path in callbacks.py
    #    recomputes n_trades/win-rate from len(ret_dist), so a sample would skew
    #    the displayed counts; they're small anyway and gzip handles them.
    _tl_test, _tl_test_n, _tl_test_tr = _cap_trade_log(bt_result.get("trade_log", []))
    _tl_tr,   _tl_tr_n,   _tl_tr_tr   = _cap_trade_log(bt_train_result.get("trade_log", []) if bt_train_result else [])

    return {
        "ohlc": {
            "dates": ohlc_dates,
            "open": _safe_list(df["open"].values),
            "high": _safe_list(df["high"].values),
            "low": _safe_list(df["low"].values),
            "close": _safe_list(df["close"].values),
        },
        "signals": {
            "dates": [str(x) for x in signal_full.index.tolist()],
            "values": [float(x) for x in signal_full.values],
        },
        "bb": bb,
        "ma": ma,
        "equity": {
            "dates": _eq_dates,
            "strategy": _eq_strat,
            "bh": _eq_bh,
        },
        "feature_importance": {
            "names": [p[0] for p in fi_pairs],
            "values": [float(p[1]) for p in fi_pairs],
        },
        "conf_matrix": cm.tolist(),
        "conf_hist": {
            "p_pos": _cp_pos,
            "p_neg": _cp_neg,
        },
        "rolling_acc": {
            "dates": _ra_dates,
            "values": _ra_vals,
        },
        "drawdown": {
            "dates": _dd_dates,
            "values": _dd_vals,
        },
        "ret_dist": [float(x) for x in bt_result["trade_returns"]],
        "ret_dist_long": [float(x) for x in bt_result["long_returns"]],
        "ret_dist_short": [float(x) for x in bt_result["short_returns"]],
        "train_ret_dist": [float(x) for x in bt_train_result["trade_returns"]] if bt_train_result else [],
        "train_ret_dist_long": [float(x) for x in bt_train_result["long_returns"]] if bt_train_result else [],
        "train_ret_dist_short": [float(x) for x in bt_train_result["short_returns"]] if bt_train_result else [],
        "trade_log": _tl_test,
        "train_trade_log": _tl_tr,
        "trade_log_total": _tl_test_n,
        "train_trade_log_total": _tl_tr_n,
        "trade_log_truncated": _tl_test_tr,
        "train_trade_log_truncated": _tl_tr_tr,
        **(pre_stats or {}),
        "metrics": metrics,
        "split_dt": split_result["split_dt"],
        "split_idx": int(split_result["split_idx"]),
        "n_train": int(split_result["n_train"]),
        "n_test": int(split_result["n_test"]),
        "feature_cols": list(feature_cols),
        "custom_figs": custom_figs or [],
    }


# ════════════════════════════════════════════════════════════════════════════
# STRATEGY FRAMEWORK v2 — Config-driven architecture
# Claude writes feature_engineering() + strategy_config(). Framework does rest.
# ════════════════════════════════════════════════════════════════════════════

import importlib

_MODEL_REGISTRY = {
    "XGBClassifier":              ("xgboost", "XGBClassifier"),
    "RandomForestClassifier":     ("sklearn.ensemble", "RandomForestClassifier"),
    "GradientBoostingClassifier": ("sklearn.ensemble", "GradientBoostingClassifier"),
    "LogisticRegression":         ("sklearn.linear_model", "LogisticRegression"),
    "ExtraTreesClassifier":       ("sklearn.ensemble", "ExtraTreesClassifier"),
    "AdaBoostClassifier":         ("sklearn.ensemble", "AdaBoostClassifier"),
}


def _build_model_from_config(config, X_train, y_train_enc):
    """Build, fit, and wrap a model from strategy_config dict."""
    model_type = config.get("model_type", "RandomForestClassifier")
    model_params = dict(config.get("model_params", {}))

    if model_type not in _MODEL_REGISTRY:
        raise ValueError(f"Unknown model_type '{model_type}'. Valid: {list(_MODEL_REGISTRY.keys())}")

    module_path, class_name = _MODEL_REGISTRY[model_type]
    mod = importlib.import_module(module_path)
    cls = getattr(mod, class_name)

    # XGBoost defaults
    if class_name == "XGBClassifier":
        model_params.setdefault("use_label_encoder", False)
        model_params.setdefault("eval_metric", "mlogloss")
        model_params.setdefault("tree_method", "hist")
        # Determinism > speed (2026-05-25). XGBoost hist with n_jobs=-1 is
        # NON-reproducible even with random_state set — the parallel histogram
        # gradient-sum order varies across threads, so the SAME code + data
        # gives a slightly different model (and backtest) every run. Forcing
        # single-thread makes training bit-reproducible so: (a) a user who
        # copies a strategy and reruns it gets identical numbers, (b) the
        # community "Live" score matches a redeploy, (c) "same code, different
        # result" support reports go away. Cost: single-threaded XGB (a few
        # seconds slower on large windows; hist is fast so it's minor). FORCED
        # (not setdefault) so the guarantee can't be silently broken by a
        # strategy passing n_jobs. Exact reproducibility holds within the
        # platform (pinned versions / same Modal image); a user's own machine
        # with different xgboost/numpy/CPU can still differ in low-order bits.
        model_params["n_jobs"] = 1

    # Common defaults
    model_params.setdefault("random_state", 42)

    from model_wrapper import ModelWrapper

    clf = cls(**model_params)
    clf.fit(X_train, y_train_enc)

    enc = LabelEncoder()
    enc.fit([-1, 0, 1])
    return ModelWrapper(clf, original_classes=enc.classes_, n_features=X_train.shape[1])


def _generate_signals(model, X, threshold):
    """Framework-owned signal generation. Deterministic threshold logic."""
    proba = model.predict_proba(X)
    classes = list(model.classes_)

    idx_pos = classes.index(1) if 1 in classes else None
    idx_neg = classes.index(-1) if -1 in classes else None

    p_pos = proba[:, idx_pos] if idx_pos is not None else np.zeros(len(X))
    p_neg = proba[:, idx_neg] if idx_neg is not None else np.zeros(len(X))

    signal_vals = np.zeros(len(X))
    signal_vals = np.where(p_pos >= threshold, 1.0, signal_vals)
    signal_vals = np.where(p_neg >= threshold, -1.0, signal_vals)
    # Both exceed: pick stronger
    both = (p_pos >= threshold) & (p_neg >= threshold)
    signal_vals[both] = np.where(p_pos[both] >= p_neg[both], 1.0, -1.0)

    return pd.Series(signal_vals, index=X.index), p_pos, p_neg


# ── Filter functions (all no-ops when config value is None) ──────────────

def _apply_direction_filter(signal, direction):
    """Zero out signals that don't match allowed direction."""
    if direction is None or direction == "both":
        return signal
    s = signal.copy()
    if direction == "long":
        s[s < 0] = 0.0
    elif direction == "short":
        s[s > 0] = 0.0
    return s


def _apply_session_filter(signal, index, session_hours):
    """Zero out signals outside session hours [start, end] UTC."""
    if session_hours is None:
        return signal
    s = signal.copy()
    start_h, end_h = session_hours[0], session_hours[1]
    hours = index.hour
    if start_h <= end_h:
        mask = (hours >= start_h) & (hours < end_h)
    else:  # wrap around midnight, e.g. [22, 6]
        mask = (hours >= start_h) | (hours < end_h)
    s[~mask] = 0.0
    return s


def _apply_atr_filter(signal, close, high, low, min_atr):
    """Zero out signals when NATR(14) is below threshold."""
    if min_atr is None:
        return signal
    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)
    atr14 = tr.ewm(com=13, adjust=False).mean()
    natr = atr14 / close.replace(0, np.nan)
    s = signal.copy()
    s[natr < min_atr] = 0.0
    return s


def _apply_trend_filter(signal, close, trend_filter):
    """Only allow signals aligned with trend. e.g. 'sma_50': longs above SMA, shorts below."""
    if trend_filter is None:
        return signal
    # Parse: "sma_50" → SMA with period 50
    parts = trend_filter.lower().replace("-", "_").split("_")
    if len(parts) >= 2 and parts[0] in ("sma", "ema"):
        period = int(parts[1])
    else:
        return signal  # unknown filter, skip
    if parts[0] == "sma":
        trend_line = close.rolling(period).mean()
    else:
        trend_line = close.ewm(span=period, adjust=False).mean()
    s = signal.copy()
    # Longs only above trend, shorts only below
    s[(s > 0) & (close < trend_line)] = 0.0
    s[(s < 0) & (close > trend_line)] = 0.0
    return s


# ── run_backtest_v2: framework-owned SL/TP/cooldown/position management ──

def run_backtest_v2(signal, close, high, low, config, capital=10000, cost=2e-5):
    """Backtest with SL/TP/cooldown/direction handling built into the engine.
    Unlike run_backtest (v1), this function handles position exits internally.

    Returns: same dict shape as run_backtest()
    """
    stop_loss = config.get("stop_loss")
    take_profit = config.get("take_profit")
    cooldown = config.get("cooldown", 0)
    on_opposite = config.get("on_opposite", "reverse")

    sig_arr = signal.values
    close_arr = close.values
    high_arr = high.values
    low_arr = low.values
    idx = signal.index
    n = len(close_arr)

    trade_returns = []
    long_returns = []
    short_returns = []
    trade_log = []
    equity_vals = np.full(n, float(capital))
    cumret = 1.0

    position = 0.0       # current direction: 1.0, -1.0, or 0.0 (flat)
    entry_price = None
    entry_bar = None      # index into arrays for entry time
    cooldown_remaining = 0

    def _log_trade(exit_bar, exit_px, ret, reason):
        trade_log.append({
            "type": "Buy" if position == 1.0 else "Sell",
            "entry_time": str(idx[entry_bar]),
            "exit_time": str(idx[exit_bar]),
            "entry_price": round(entry_price, 5),
            "exit_price": round(exit_px, 5),
            "pnl": round(position * (exit_px - entry_price), 5),
            "pnl_pct": round(ret * 100, 3),
            "exit_reason": reason,
        })

    for i in range(n):
        c = close_arr[i]
        h = high_arr[i]
        lo = low_arr[i]
        s = sig_arr[i]

        # 1. Check SL/TP if in trade
        if position != 0.0 and entry_price is not None:
            hit_sl = False
            hit_tp = False
            exit_price = None

            if position == 1.0:  # long
                if stop_loss is not None and lo <= entry_price * (1 - stop_loss):
                    hit_sl = True
                    exit_price = entry_price * (1 - stop_loss)
                elif take_profit is not None and h >= entry_price * (1 + take_profit):
                    hit_tp = True
                    exit_price = entry_price * (1 + take_profit)
            else:  # short
                if stop_loss is not None and h >= entry_price * (1 + stop_loss):
                    hit_sl = True
                    exit_price = entry_price * (1 + stop_loss)
                elif take_profit is not None and lo <= entry_price * (1 - take_profit):
                    hit_tp = True
                    exit_price = entry_price * (1 - take_profit)

            if hit_sl or hit_tp:
                ret = float(position * (exit_price - entry_price) / entry_price - cost)
                trade_returns.append(ret)
                if position == 1.0:
                    long_returns.append(ret)
                else:
                    short_returns.append(ret)
                _log_trade(i, exit_price, ret, "SL" if hit_sl else "TP")
                cumret *= (1 + ret)
                position = 0.0
                entry_price = None
                entry_bar = None
                cooldown_remaining = cooldown
                equity_vals[i] = capital * cumret
                continue

        # 2. Cooldown
        if cooldown_remaining > 0:
            cooldown_remaining -= 1
            equity_vals[i] = capital * cumret
            continue

        # 3. Signal processing
        if s != 0.0:
            if position == 0.0:
                # Open new trade
                position = s
                entry_price = c
                entry_bar = i
            elif s != position:
                # Opposite signal
                if on_opposite == "reverse":
                    # Close current + open opposite
                    ret = float(position * (c - entry_price) / entry_price - cost)
                    trade_returns.append(ret)
                    if position == 1.0:
                        long_returns.append(ret)
                    else:
                        short_returns.append(ret)
                    _log_trade(i, c, ret, "signal")
                    cumret *= (1 + ret)
                    position = s
                    entry_price = c
                    entry_bar = i
                else:  # close_only
                    # Close current, go flat
                    ret = float(position * (c - entry_price) / entry_price - cost)
                    trade_returns.append(ret)
                    if position == 1.0:
                        long_returns.append(ret)
                    else:
                        short_returns.append(ret)
                    _log_trade(i, c, ret, "close_only")
                    cumret *= (1 + ret)
                    position = 0.0
                    entry_price = None
                    entry_bar = None
                    cooldown_remaining = cooldown

        equity_vals[i] = capital * cumret

    # Close last open trade at final close
    if position != 0.0 and entry_price is not None and n > 0 and entry_price != 0:
        c = close_arr[-1]
        ret = float(position * (c - entry_price) / entry_price - cost)
        trade_returns.append(ret)
        if position == 1.0:
            long_returns.append(ret)
        else:
            short_returns.append(ret)
        _log_trade(n - 1, c, ret, "end")
        cumret *= (1 + ret)
        equity_vals[-1] = capital * cumret

    # Bar returns for Sharpe (approximate)
    bar_returns = np.zeros(n)
    for i in range(1, n):
        if close_arr[i - 1] != 0 and sig_arr[i - 1] != 0:
            bar_returns[i] = sig_arr[i - 1] * (close_arr[i] - close_arr[i - 1]) / close_arr[i - 1]

    return {
        "equity": pd.Series(equity_vals, index=close.index),
        "trade_returns": trade_returns,
        "long_returns": long_returns,
        "short_returns": short_returns,
        "bar_returns": bar_returns,
        "trade_log": trade_log,
    }


# ── run_strategy: the v2 orchestrator ────────────────────────────────────

def run_strategy(feature_fn, config_fn, data_path, start_date="", end_date="",
                 validation_date="", train_split=0.7, register_model_fn=None):
    """Config-driven strategy execution. Claude writes feature_fn + config_fn,
    framework does everything else.

    Returns: results dict (same format as webapp expects)
    """
    config = config_fn()

    # Auto-correct SL/TP if Claude passed percentage instead of decimal
    for _key in ("stop_loss", "take_profit"):
        _val = config.get(_key)
        if _val is not None and _val > 0.1:  # >10% is almost certainly a percentage
            config[_key] = _val / 100.0
            print(f"[strategy] Auto-corrected {_key}: {_val} -> {config[_key]} (was percentage, converted to decimal)")

    # 1. Load data
    df, close, open_, high, low = load_ohlc(data_path, start_date, end_date)

    # 2. Feature engineering (Claude's function)
    df = feature_fn(df, close, open_, high, low)
    close = df["close"]
    open_ = df["open"]
    high = df["high"]
    low = df["low"]

    # 3. Warm-up detection: drop rows where features have NaN BEFORE any fill
    feature_cols = [c for c in df.columns if c not in ("open", "high", "low", "close")]
    raw_nans = df[feature_cols].isna().any(axis=1)
    valid_rows = ~raw_nans
    if valid_rows.any():
        first_valid = valid_rows.idxmax()
        if raw_nans.loc[:first_valid].any():
            df = df.loc[first_valid:].copy()
            close = df["close"]
            open_ = df["open"]
            high = df["high"]
            low = df["low"]

    # 4. Target
    horizon = config.get("target_horizon", 4)
    target = make_target(close, horizon=horizon)

    # 5. Split (ffill only within each partition — no bfill leak)
    mask = target.notna()
    df = df[mask].copy()
    target = target[mask]
    close = df["close"]
    high = df["high"]
    low = df["low"]

    X = df[feature_cols].copy()
    X = X.replace([np.inf, -np.inf], np.nan)

    if validation_date:
        split_idx = len(df[df.index <= validation_date])
    else:
        split_idx = int(len(df) * train_split)
    split_idx = max(1, min(split_idx, len(df) - 1))

    # ffill within train and test separately (no leak)
    X_train = X.iloc[:split_idx].ffill().fillna(0.0)
    X_test = X.iloc[split_idx:].ffill().fillna(0.0)
    X = pd.concat([X_train, X_test])

    y_train = target.iloc[:split_idx]
    y_test = target.iloc[split_idx:]
    close_train = close.iloc[:split_idx]
    close_test = close.iloc[split_idx:]
    high_test = high.iloc[split_idx:]
    low_test = low.iloc[split_idx:]

    enc = LabelEncoder()
    enc.fit([-1, 0, 1])
    y_train_enc = enc.transform(y_train)
    y_test_enc = enc.transform(y_test)

    split_dt = str(df.index[split_idx])

    sp = {
        "df": df, "X_train": X_train, "X_test": X_test,
        "y_train": y_train, "y_test": y_test,
        "y_train_enc": y_train_enc, "y_test_enc": y_test_enc,
        "enc": enc,
        "close": close, "close_train": close_train, "close_test": close_test,
        "split_idx": split_idx, "split_dt": split_dt,
        "n_train": len(X_train), "n_test": len(X_test),
    }

    # 6. Build model from config
    model = _build_model_from_config(config, X_train, y_train_enc)

    # 7. Generate signals
    threshold = config.get("signal_threshold", 0.55)
    signal_train, p_pos_train, p_neg_train = _generate_signals(model, X_train, threshold)
    signal_test, p_pos_test, p_neg_test = _generate_signals(model, X_test, threshold)

    # 8. Apply filters (order: direction → session → ATR → trend)
    direction = config.get("direction", "both")
    signal_test = _apply_direction_filter(signal_test, direction)
    signal_train = _apply_direction_filter(signal_train, direction)

    session_filter = config.get("session_filter")
    signal_test = _apply_session_filter(signal_test, signal_test.index, session_filter)
    signal_train = _apply_session_filter(signal_train, signal_train.index, session_filter)

    min_atr = config.get("min_atr")
    if min_atr is not None:
        signal_test = _apply_atr_filter(signal_test, close_test, high_test, low_test, min_atr)

    trend_filter = config.get("trend_filter")
    if trend_filter is not None:
        signal_test = _apply_trend_filter(signal_test, close_test, trend_filter)

    signal_full = pd.concat([signal_train, signal_test])

    # 9. Backtest with SL/TP/cooldown (test + train)
    high_train = high.iloc[:split_idx]
    low_train = low.iloc[:split_idx]
    has_risk = (config.get("stop_loss") is not None or
                config.get("take_profit") is not None or
                config.get("cooldown", 0) > 0 or
                config.get("on_opposite", "reverse") != "reverse")

    if has_risk:
        bt = run_backtest_v2(signal_test, close_test, high_test, low_test, config, capital=10000)
        bt_train = run_backtest_v2(signal_train, close_train, high_train, low_train, config, capital=10000)
    else:
        bt = run_backtest(signal_test, close_test, capital=10000)
        bt_train = run_backtest(signal_train, close_train, capital=10000)

    # 10. Metrics
    metrics = compute_metrics(bt, close_test, capital=10000)

    # 11. Pre-compute all trade stats (single source of truth)
    pre_stats = {
        "train_stats": compute_trade_stats(bt_train.get("trade_returns", []), capital=10000),
        "test_stats":  compute_trade_stats(bt.get("trade_returns", []), capital=10000),
        "long_stats":  compute_trade_stats(bt.get("long_returns", []), capital=10000),
        "short_stats": compute_trade_stats(bt.get("short_returns", []), capital=10000),
    }

    # 12. Register model
    if register_model_fn is not None:
        register_model_fn(model)

    # 13. Build return dict
    return build_return_dict(sp, bt, metrics, model, feature_cols,
                             signal_full, p_pos_test, p_neg_test, custom_figs=[],
                             bt_train_result=bt_train, pre_stats=pre_stats)

# ── End strategy_utils ──


DATA_PATH = '/root/Desktop/QuantifyMe/data/ohlc/EURUSD_15min.parquet'
START_DATE = '2026-04-15'
END_DATE = '2026-05-25'
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

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

    # --- RSI(14) ---
    period = 14
    delta = close.diff()
    gain = delta.clip(lower=0)
    loss = -delta.clip(upper=0)
    avg_gain = gain.ewm(alpha=1.0 / period, min_periods=period, adjust=False).mean()
    avg_loss = loss.ewm(alpha=1.0 / period, min_periods=period, adjust=False).mean()
    rs = avg_gain / avg_loss.replace(0, np.nan)
    df["rsi_14"] = 100.0 - (100.0 / (1.0 + rs))

    # --- RSI crossover signals: cross up from below 30, cross down from above 70 ---
    rsi_prev = df["rsi_14"].shift(1)
    df["rsi_cross_up30"] = np.where(
        (rsi_prev < 30) & (df["rsi_14"] >= 30), 1.0, 0.0
    )
    df["rsi_cross_dn70"] = np.where(
        (rsi_prev > 70) & (df["rsi_14"] <= 70), 1.0, 0.0
    )

    # --- RSI distance from thresholds (signed) ---
    df["rsi_dist_30"] = df["rsi_14"] - 30.0
    df["rsi_dist_70"] = df["rsi_14"] - 70.0
    df["rsi_dist_50"] = df["rsi_14"] - 50.0

    # --- RSI(5) for short-term momentum ---
    period5 = 5
    delta5 = close.diff()
    gain5 = delta5.clip(lower=0)
    loss5 = -delta5.clip(upper=0)
    avg_gain5 = gain5.ewm(alpha=1.0 / period5, min_periods=period5, adjust=False).mean()
    avg_loss5 = loss5.ewm(alpha=1.0 / period5, min_periods=period5, adjust=False).mean()
    rs5 = avg_gain5 / avg_loss5.replace(0, np.nan)
    df["rsi_5"] = 100.0 - (100.0 / (1.0 + rs5))

    # --- RSI(28) for longer-term regime ---
    period28 = 28
    delta28 = close.diff()
    gain28 = delta28.clip(lower=0)
    loss28 = -delta28.clip(upper=0)
    avg_gain28 = gain28.ewm(alpha=1.0 / period28, min_periods=period28, adjust=False).mean()
    avg_loss28 = loss28.ewm(alpha=1.0 / period28, min_periods=period28, adjust=False).mean()
    rs28 = avg_gain28 / avg_loss28.replace(0, np.nan)
    df["rsi_28"] = 100.0 - (100.0 / (1.0 + rs28))

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

    # --- ATR(14) for volatility ---
    tr1 = high - low
    tr2 = (high - close.shift(1)).abs()
    tr3 = (low - close.shift(1)).abs()
    true_range = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
    df["atr_14"] = true_range.ewm(alpha=1.0 / 14, min_periods=14, adjust=False).mean()
    df["natr_14"] = np.where(close != 0, df["atr_14"] / close, np.nan)

    # --- MACD (12, 26, 9) ---
    ema12 = close.ewm(span=12, min_periods=12, adjust=False).mean()
    ema26 = close.ewm(span=26, min_periods=26, adjust=False).mean()
    macd_line = ema12 - ema26
    signal_line = macd_line.ewm(span=9, min_periods=9, adjust=False).mean()
    df["macd"] = macd_line
    df["macd_signal"] = signal_line
    df["macd_hist"] = macd_line - signal_line
    df["macd_cross_up"] = np.where(
        (macd_line.shift(1) < signal_line.shift(1)) & (macd_line >= signal_line), 1.0, 0.0
    )
    df["macd_cross_dn"] = np.where(
        (macd_line.shift(1) > signal_line.shift(1)) & (macd_line <= signal_line), 1.0, 0.0
    )

    # --- Stochastic Oscillator (14, 3) ---
    stoch_period = 14
    lowest_low = low.rolling(stoch_period).min()
    highest_high = high.rolling(stoch_period).max()
    denom = (highest_high - lowest_low).replace(0, np.nan)
    stoch_k = 100.0 * (close - lowest_low) / denom
    stoch_d = stoch_k.rolling(3).mean()
    df["stoch_k"] = stoch_k
    df["stoch_d"] = stoch_d
    df["stoch_cross_up"] = np.where(
        (stoch_k.shift(1) < stoch_d.shift(1)) & (stoch_k >= stoch_d) & (stoch_k < 30), 1.0, 0.0
    )
    df["stoch_cross_dn"] = np.where(
        (stoch_k.shift(1) > stoch_d.shift(1)) & (stoch_k <= stoch_d) & (stoch_k > 70), 1.0, 0.0
    )

    # --- SMA trend features ---
    df["sma_20"] = close.rolling(20).mean()
    df["sma_50"] = close.rolling(50).mean()
    df["sma_200"] = close.rolling(200).mean()
    df["price_vs_sma20"] = (close - df["sma_20"]) / df["sma_20"].replace(0, np.nan)
    df["price_vs_sma50"] = (close - df["sma_50"]) / df["sma_50"].replace(0, np.nan)
    df["sma20_vs_sma50"] = (df["sma_20"] - df["sma_50"]) / df["sma_50"].replace(0, np.nan)

    # --- Price momentum (returns) ---
    df["ret_1"] = close.pct_change(1)
    df["ret_4"] = close.pct_change(4)
    df["ret_8"] = close.pct_change(8)
    df["ret_16"] = close.pct_change(16)

    # --- Candle body and wick features ---
    body = (close - open_).abs()
    upper_wick = high - pd.concat([close, open_], axis=1).max(axis=1)
    lower_wick = pd.concat([close, open_], axis=1).min(axis=1) - low
    candle_range = (high - low).replace(0, np.nan)
    df["body_ratio"] = body / candle_range
    df["upper_wick_ratio"] = upper_wick / candle_range
    df["lower_wick_ratio"] = lower_wick / candle_range
    df["candle_direction"] = np.where(close >= open_, 1.0, -1.0)

    # --- Volume of consecutive bars in same direction ---
    df["consec_up"] = (
        df["candle_direction"]
        .groupby((df["candle_direction"] != df["candle_direction"].shift(1)).cumsum())
        .cumcount() + 1
    ) * np.where(df["candle_direction"] > 0, 1.0, 0.0)
    df["consec_dn"] = (
        df["candle_direction"]
        .groupby((df["candle_direction"] != df["candle_direction"].shift(1)).cumsum())
        .cumcount() + 1
    ) * np.where(df["candle_direction"] < 0, 1.0, 0.0)

    # --- RSI slope (rate of change) ---
    df["rsi_slope_3"] = df["rsi_14"].diff(3)
    df["rsi_slope_5"] = df["rsi_14"].diff(5)

    # --- Rolling high/low channel breakout context ---
    df["high_20"] = high.rolling(20).max()
    df["low_20"] = low.rolling(20).min()
    df["price_pos_in_range"] = np.where(
        (df["high_20"] - df["low_20"]) != 0,
        (close - df["low_20"]) / (df["high_20"] - df["low_20"]),
        0.5
    )

    # --- RSI oversold/overbought binary flags ---
    df["rsi_oversold"] = np.where(df["rsi_14"] < 30, 1.0, 0.0)
    df["rsi_overbought"] = np.where(df["rsi_14"] > 70, 1.0, 0.0)
    df["rsi_neutral"] = np.where((df["rsi_14"] >= 40) & (df["rsi_14"] <= 60), 1.0, 0.0)

    # --- Rolling RSI min/max to track extremes ---
    df["rsi_min_10"] = df["rsi_14"].rolling(10).min()
    df["rsi_max_10"] = df["rsi_14"].rolling(10).max()
    df["rsi_range_10"] = df["rsi_max_10"] - df["rsi_min_10"]

    # --- Time-of-day features (sin/cos encoding for session awareness) ---
    if hasattr(df.index, "hour"):
        hour = df.index.hour + df.index.minute / 60.0
        df["hour_sin"] = np.sin(2.0 * np.pi * hour / 24.0)
        df["hour_cos"] = np.cos(2.0 * np.pi * 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": "RSI Crossover Mean-Reversion (XGBoost, Sharpe)",
        "model_type": "XGBClassifier",
        "model_params": {
            "n_estimators": 400,
            "max_depth": 4,
            "learning_rate": 0.04,
            "subsample": 0.8,
            "colsample_bytree": 0.7,
            "min_child_weight": 3,
            "gamma": 0.1,
            "reg_alpha": 0.05,
            "reg_lambda": 1.2,
            "objective": "binary:logistic",
            "n_jobs": -1,
            "random_state": 42,
        },
        "signal_threshold": 0.55,
        "direction": "both",
        "stop_loss": 0.004,
        "take_profit": 0.008,
        "cooldown": 0,
        "max_positions": 1,
        "on_opposite": "reverse",
        "session_filter": [7, 18],
        "min_atr": None,
        "trend_filter": None,
        "target_horizon": 4,
        "objective": (
            "Maximize Sharpe ratio by capturing mean-reversion when RSI crosses "
            "back from oversold (<30) or overbought (>70) extremes. XGBoost with "
            "moderate depth and shrinkage prevents overfitting on the short EUR/USD "
            "window. A 2:1 TP:SL ratio (0.8%/0.4%) on 15-min bars targets clean "
            "risk-adjusted returns. Session filter restricts to liquid London/NY hours."
        ),
        "notes": (
            "Feature set combines the core RSI crossover signal with multi-period RSI, "
            "MACD histogram, Stochastic, Bollinger %B, ATR normalised volatility, "
            "price momentum across 4 horizons, candle structure ratios, and time encoding. "
            "colsample_bytree=0.7 adds diversity across trees; subsample=0.8 reduces "
            "variance. min_child_weight=3 avoids splitting on noisy one-off RSI spikes. "
            "No trend_filter so the model can express both long and short mean-reversion "
            "signals symmetrically via the 'both' direction setting."
        ),
    }

# ── Framework v2: auto-generated wrapper ──
def train_and_backtest():
    _vd = VALIDATION_DATE if 'VALIDATION_DATE' in globals() else ''
    _ts = TRAIN_SPLIT if 'TRAIN_SPLIT' in globals() else 0.7
    return run_strategy(
        feature_engineering, strategy_config,
        DATA_PATH, START_DATE, END_DATE,
        _vd, _ts,
        register_model_fn=register_model
    )

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 01:16:23
# 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(upper=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 = avg_gain / avg_loss.replace(0, np.nan)
    df["rsi_14"] = 100 - (100 / (1 + rs))

    # RSI derived signals
    df["rsi_overbought"] = np.where(df["rsi_14"] > 70, 1, 0)
    df["rsi_oversold"]   = np.where(df["rsi_14"] < 30, 1, 0)
    df["rsi_mid_cross"]  = np.where(df["rsi_14"] > 50, 1, -1)

    # RSI momentum (rate of change of RSI)
    df["rsi_roc"] = df["rsi_14"].diff(3)

    # ── 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()
    macd_hist   = macd_line - signal_line

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

    # MACD derived signals
    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_accel"] = macd_hist.diff()

    # ── ATR (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=13, min_periods=14, adjust=False).mean()
    df["natr_14"] = df["atr_14"] / close

    # ── Bollinger Bands (20, 2) ──────────────────────────────────────────────
    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_position"] = (close - bb_lower) / (bb_upper - bb_lower).replace(0, np.nan)
    df["bb_squeeze"]  = np.where(df["bb_width"] < df["bb_width"].rolling(50).mean(), 1, 0)

    # ── Trend / SMA filters ──────────────────────────────────────────────────
    df["sma_20"]  = sma_20
    df["sma_50"]  = close.rolling(50).mean()
    df["sma_200"] = close.rolling(200).mean()
    df["price_above_sma50"]  = np.where(close > df["sma_50"],  1, -1)
    df["price_above_sma200"] = np.where(close > df["sma_200"], 1, -1)
    df["sma50_above_sma200"] = np.where(df["sma_50"] > df["sma_200"], 1, -1)

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

    # ── Candlestick / price structure ────────────────────────────────────────
    df["body"]        = (close - open_) / close
    df["upper_wick"]  = (high - close.clip(lower=open_)) / close
    df["lower_wick"]  = (open_.clip(upper=close) - low)  / close
    df["hl_range"]    = (high - low) / close
    df["gap"]         = (open_ - close.shift(1)) / close.shift(1)

    # ── Volume-free spread proxy ─────────────────────────────────────────────
    df["spread_ratio"] = df["hl_range"] / df["atr_14"].replace(0, np.nan)

    # ── Stochastic RSI proxy ─────────────────────────────────────────────────
    rsi_min = df["rsi_14"].rolling(14).min()
    rsi_max = df["rsi_14"].rolling(14).max()
    df["stoch_rsi"] = (df["rsi_14"] - rsi_min) / (rsi_max - rsi_min).replace(0, np.nan)

    # ── Williams %R (14) ─────────────────────────────────────────────────────
    highest_high = high.rolling(14).max()
    lowest_low   = low.rolling(14).min()
    df["williams_r"] = -100 * (highest_high - close) / (highest_high - lowest_low).replace(0, np.nan)

    # ── Lagged features ──────────────────────────────────────────────────────
    for lag in [1, 2, 3, 4]:
        df[f"rsi_lag{lag}"]       = df["rsi_14"].shift(lag)
        df[f"macd_hist_lag{lag}"] = df["macd_hist"].shift(lag)
        df[f"roc4_lag{lag}"]      = df["roc_4"].shift(lag)

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

# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "NZD/USD MACD+RSI Gradient Boosting Risk-Adjusted",
        "model_type":       "GradientBoostingClassifier",
        "model_params":     {
            "n_estimators":     400,
            "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":   [0, 23],
        "min_atr":          None,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective": (
            "Maximize risk-adjusted return (Sharpe/Calmar) on NZD/USD 15-min data. "
            "GradientBoostingClassifier chosen for strong generalisation with tabular features, "
            "low learning rate + early stopping prevents overfitting. "
            "SL=0.5%, TP=1.0% gives 1:2 R:R ratio. Threshold=0.55 filters marginal signals."
        ),
        "notes": (
            "Core features: RSI-14, MACD(12,26,9) with histogram momentum. "
            "Supplementary: ATR, Bollinger Bands, SMA trend, Williams %R, StochRSI, "
            "candlestick structure, lagged RSI/MACD/ROC. "
            "reverse on opposite signal captures trend continuation. "
            "target_horizon=4 bars (1 hour) aligns with typical MACD/RSI signal duration."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  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        : 2026-05-05 09:59:46
# Model            : XGBoost
# 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 = "2025-04-23"
END_DATE   = "2026-04-23"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7

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

    # ── SMA 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, 2.0) ───────────────────────────────────────────
    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
    bb_range = bb_upper - bb_lower
    df["bb_mid"]   = bb_mid
    df["bb_upper"] = bb_upper
    df["bb_lower"] = bb_lower
    df["bb_width"] = bb_range / bb_mid
    df["bb_pct"]   = (close - bb_lower) / bb_range.replace(0, np.nan)

    # ── 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  = avg_gain / avg_loss.replace(0, np.nan)
    df["rsi_14"] = 100.0 - (100.0 / (1.0 + rs))

    # ── 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_line"]   = macd_line
    df["macd_signal"] = signal_line
    df["macd_hist"]   = macd_line - signal_line

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

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

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

    # Log return
    df["log_ret_1"] = np.log(close / close.shift(1))

    # Candle body and wick ratios
    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_dir"]       = np.where(close >= open_, 1.0, -1.0)

    # Volume / volatility proxy: rolling std of returns
    ret = close.pct_change()
    df["vol_8"]  = ret.rolling(8).std()
    df["vol_20"] = ret.rolling(20).std()

    # RSI-derived features
    df["rsi_dist_50"]  = df["rsi_14"] - 50.0
    df["rsi_overbought"] = np.where(df["rsi_14"] > 70, 1.0, 0.0)
    df["rsi_oversold"]   = np.where(df["rsi_14"] < 30, 1.0, 0.0)

    # MACD cross signal
    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
        )
    )

    # BB squeeze: narrow bands relative to recent average
    df["bb_squeeze"] = np.where(df["bb_width"] < df["bb_width"].rolling(50).mean(), 1.0, 0.0)

    # Price relative to SMA crossovers
    df["sma20_above_sma50"]  = np.where(df["sma_20"] > df["sma_50"],  1.0, -1.0)
    df["sma50_above_sma200"] = np.where(df["sma_50"] > df["sma_200"], 1.0, -1.0)

    # High/low channel breakout features
    df["high_20"]   = high.rolling(20).max()
    df["low_20"]    = low.rolling(20).min()
    df["chan_pos"]  = (close - df["low_20"]) / (df["high_20"] - df["low_20"]).replace(0, np.nan)

    # Lagged RSI and MACD hist
    df["rsi_14_lag1"] = df["rsi_14"].shift(1)
    df["rsi_14_lag4"] = df["rsi_14"].shift(4)
    df["macd_hist_lag1"] = df["macd_hist"].shift(1)

    # ATR trend: expanding vs contracting volatility
    df["atr_ratio"] = df["atr_14"] / df["atr_14"].rolling(50).mean()

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

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title": "EUR/USD XGBoost SMA+RSI+MACD+BB Trend Rider",
        "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":  5,
            "gamma":             0.2,
            "reg_alpha":         0.1,
            "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":   [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. "
            "XGBoost with moderate depth and regularisation to avoid overfitting on "
            "noisy FX data. Conservative SL/TP ratio of 1:2 improves expectancy. "
            "Session filter keeps the model active during liquid London/NY overlap. "
            "Min ATR filter avoids low-volatility noise. SMA-50 trend filter aligns "
            "trades with the prevailing medium-term trend, reducing whipsaw."
        ),
        "notes": (
            "Feature set: SMA 20/50/200 distances, BB width/pct, RSI 14, MACD histogram, "
            "ATR/NATR, multi-horizon momentum, candle structure, volatility, channel position, "
            "lagged indicators, and cross-over binary signals. "
            "Hyperparameters tuned for bias-variance balance: shallow trees (depth 4), "
            "high n_estimators with low learning rate, stochastic sampling, and L1/L2 "
            "regularisation reduce overfitting. Threshold 0.54 slightly above default to "
            "filter marginal signals while maintaining trade frequency."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 02:51:04
# Model            : XGBoost
# 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 ──────────────────────────────────────────────────────────────
    period = 14
    delta = close.diff()
    gain = delta.clip(lower=0)
    loss = -delta.clip(upper=0)
    avg_gain = gain.ewm(com=period - 1, min_periods=period).mean()
    avg_loss = loss.ewm(com=period - 1, min_periods=period).mean()
    rs = avg_gain / avg_loss.replace(0, np.nan)
    df["rsi_14"] = 100 - (100 / (1 + rs))

    # RSI derived features
    df["rsi_14_norm"] = (df["rsi_14"] - 50) / 50          # centred & scaled
    df["rsi_14_ob"]   = np.where(df["rsi_14"] > 70, 1, 0) # overbought flag
    df["rsi_14_os"]   = np.where(df["rsi_14"] < 30, 1, 0) # oversold flag
    df["rsi_14_mom"]  = df["rsi_14"].diff(3)               # 3-bar momentum

    # ── 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()
    macd_hist   = macd_line - signal_line

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

    # MACD derived features
    df["macd_hist_mom"]   = macd_hist.diff(2)              # histogram momentum
    df["macd_cross_bull"] = np.where(
        (macd_line > signal_line) & (macd_line.shift(1) <= signal_line.shift(1)), 1, 0
    )
    df["macd_cross_bear"] = np.where(
        (macd_line < signal_line) & (macd_line.shift(1) >= signal_line.shift(1)), 1, 0
    )
    df["macd_zero_cross"] = np.where(
        (macd_line > 0) & (macd_line.shift(1) <= 0), 1,
        np.where((macd_line < 0) & (macd_line.shift(1) >= 0), -1, 0)
    )

    # ── Additional price-action features ────────────────────────────────────
    # ATR (14) for volatility context
    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
    df["natr_14"] = atr_14 / close                         # normalised ATR

    # Bollinger Bands (20, 2) — mean-reversion context
    sma_20  = close.rolling(20).mean()
    std_20  = close.rolling(20).std()
    bb_up   = sma_20 + 2 * std_20
    bb_lo   = sma_20 - 2 * std_20
    df["bb_pct"]   = (close - bb_lo) / (bb_up - bb_lo + 1e-12)  # 0-1 position
    df["bb_width"] = (bb_up - bb_lo) / sma_20                   # band width

    # SMA filters
    df["sma_20"]  = sma_20
    df["sma_50"]  = close.rolling(50).mean()
    df["price_vs_sma20"] = (close - sma_20) / (sma_20 + 1e-12)
    df["price_vs_sma50"] = (close - df["sma_50"]) / (df["sma_50"] + 1e-12)

    # Rate of change
    df["roc_5"]  = close.pct_change(5)
    df["roc_10"] = close.pct_change(10)
    df["roc_20"] = close.pct_change(20)

    # Candlestick body / shadow ratios
    body        = (close - open_).abs()
    candle_rng  = (high - low).replace(0, np.nan)
    df["body_ratio"]   = body / candle_rng
    df["upper_shadow"]  = (high - pd.concat([close, open_], axis=1).max(axis=1)) / candle_rng
    df["lower_shadow"]  = (pd.concat([close, open_], axis=1).min(axis=1) - low) / candle_rng
    df["bar_direction"] = np.where(close >= open_, 1, -1)

    # Volume-proxy: realised range / ATR ratio
    df["range_vs_atr"] = candle_rng / (atr_14 + 1e-12)

    # Stochastic %K (14)
    lowest_14  = low.rolling(14).min()
    highest_14 = high.rolling(14).max()
    stoch_k    = 100 * (close - lowest_14) / (highest_14 - lowest_14 + 1e-12)
    df["stoch_k"]   = stoch_k
    df["stoch_d"]   = stoch_k.rolling(3).mean()
    df["stoch_diff"] = df["stoch_k"] - df["stoch_d"]

    # RSI × MACD interaction
    df["rsi_macd_interact"] = df["rsi_14_norm"] * macd_hist

    # Lagged features (1 and 2 bars back) for key signals
    for col in ["rsi_14_norm", "macd_hist", "bb_pct", "roc_5"]:
        df[f"{col}_lag1"] = df[col].shift(1)
        df[f"{col}_lag2"] = df[col].shift(2)

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


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "NZD/USD MACD+RSI Momentum (XGBoost, Risk-Adj)",
        "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",
            "tree_method":        "hist",
            "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":   [0, 23],
        "min_atr":          None,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective": (
            "Maximise risk-adjusted return (Sharpe/Calmar) on NZD/USD 15-min data. "
            "XGBoost chosen for its strong performance on tabular financial data. "
            "Moderate depth (4) and high regularisation (gamma, alpha, lambda) prevent "
            "overfitting on a relatively small forex dataset. Subsample + colsample_bytree "
            "add stochastic diversity. SL 0.5% / TP 1.0% gives a 1:2 R:R ratio to support "
            "positive expectancy even with a sub-60% win rate. Threshold 0.55 filters marginal "
            "signals while keeping trade frequency acceptable. Target horizon of 4 bars (1 hour) "
            "aligns with typical MACD/RSI signal resolution on 15-min charts."
        ),
        "notes": (
            "Features: RSI-14 (raw, normalised, OB/OS flags, momentum), MACD(12,26,9) "
            "(line, signal, histogram, crosses, zero-cross), Bollinger Bands %B & width, "
            "ATR/NATR, SMA20/50 price deviations, Stochastic %K/%D, ROC(5/10/20), "
            "candlestick body/shadow ratios, RSI×MACD interaction term, and lagged "
            "versions (lag1, lag2) of key signals to capture short-term persistence."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  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 02:56:13
# 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 = "2025-04-24"
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"]

    # ── Price vs SMA alignment score (how many SMAs price is above) ───────
    above_20  = np.where(close > df["sma_20"],  1, -1)
    above_50  = np.where(close > df["sma_50"],  1, -1)
    above_200 = np.where(close > df["sma_200"], 1, -1)
    df["sma_alignment"] = (above_20 + above_50 + above_200).astype(float)

    # ── Returns at multiple horizons ──────────────────────────────────────
    for lag in [1, 2, 4, 8, 16]:
        df[f"ret_{lag}"] = close.pct_change(lag)

    # ── Log returns ───────────────────────────────────────────────────────
    df["log_ret_1"] = np.log(close / close.shift(1))
    df["log_ret_4"] = np.log(close / close.shift(4))

    # ── ATR (14-period) ───────────────────────────────────────────────────
    tr1 = high - low
    tr2 = (high - close.shift(1)).abs()
    tr3 = (low  - close.shift(1)).abs()
    true_range = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
    atr_14 = true_range.rolling(14).mean()
    df["atr_14"]  = atr_14
    df["natr_14"] = atr_14 / close

    # ── ATR ratio (current TR vs average — volatility burst) ──────────────
    df["atr_ratio"] = true_range / atr_14

    # ── Bollinger Bands (20, 2) ───────────────────────────────────────────
    bb_mid  = close.rolling(20).mean()
    bb_std  = close.rolling(20).std()
    bb_upper = bb_mid + 2.0 * bb_std
    bb_lower = bb_mid - 2.0 * bb_std
    bb_width = (bb_upper - bb_lower) / bb_mid
    df["bb_pct_b"]  = (close - bb_lower) / (bb_upper - bb_lower + 1e-12)
    df["bb_width"]  = bb_width
    df["bb_zscore"] = (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 momentum ─────────────────────────────────────────────────────
    df["rsi_14_diff"] = df["rsi_14"].diff(2)

    # ── 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_line"]   = macd_line
    df["macd_signal"] = signal_line
    df["macd_hist"]   = macd_line - signal_line
    df["macd_hist_diff"] = df["macd_hist"].diff(1)

    # ── Stochastic %K / %D (14, 3) ───────────────────────────────────────
    low_14  = low.rolling(14).min()
    high_14 = high.rolling(14).max()
    stoch_k = 100 * (close - low_14) / (high_14 - low_14 + 1e-12)
    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

    # ── Williams %R (14) ─────────────────────────────────────────────────
    df["williams_r"] = -100 * (high_14 - close) / (high_14 - low_14 + 1e-12)

    # ── CCI (20) ─────────────────────────────────────────────────────────
    typical_price = (high + low + close) / 3
    tp_sma  = typical_price.rolling(20).mean()
    tp_mad  = typical_price.rolling(20).apply(lambda x: np.mean(np.abs(x - x.mean())), raw=True)
    df["cci_20"] = (typical_price - tp_sma) / (0.015 * tp_mad + 1e-12)

    # ── Rate of Change (10) ───────────────────────────────────────────────
    df["roc_10"] = (close - close.shift(10)) / (close.shift(10) + 1e-12) * 100

    # ── Volume / candle-body features (no volume data assumed) ────────────
    df["body_size"]   = (close - open_).abs() / (high - low + 1e-12)
    df["upper_wick"]  = (high - np.maximum(close, open_)) / (high - low + 1e-12)
    df["lower_wick"]  = (np.minimum(close, open_) - low)  / (high - low + 1e-12)
    df["is_bullish"]  = np.where(close > open_, 1.0, 0.0)

    # ── Rolling volatility (realised) ────────────────────────────────────
    df["vol_8"]  = df["log_ret_1"].rolling(8).std()
    df["vol_20"] = df["log_ret_1"].rolling(20).std()
    df["vol_ratio"] = df["vol_8"] / (df["vol_20"] + 1e-12)

    # ── High-low range relative to SMA ───────────────────────────────────
    df["hl_range_sma20"] = (high - low) / (df["sma_20"] + 1e-12)

    # ── Price position within recent n-bar range ──────────────────────────
    for n in [8, 20]:
        roll_low  = low.rolling(n).min()
        roll_high = high.rolling(n).max()
        df[f"price_pos_{n}"] = (close - roll_low) / (roll_high - roll_low + 1e-12)

    # ── Lagged returns as autoregressive features ─────────────────────────
    for lag in [1, 2, 3, 4, 8]:
        df[f"lag_ret_{lag}"] = df["ret_1"].shift(lag)

    # ── Distance from 20-bar high/low ────────────────────────────────────
    df["dist_high_20"] = (high.rolling(20).max() - close) / (close + 1e-12)
    df["dist_low_20"]  = (close - low.rolling(20).min())  / (close + 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 XGBoost",
        "model_type":       "XGBClassifier",
        "model_params":     {
            "n_estimators":     500,
            "max_depth":        4,
            "learning_rate":    0.03,
            "subsample":        0.8,
            "colsample_bytree": 0.7,
            "min_child_weight": 3,
            "gamma":            0.1,
            "reg_alpha":        0.05,
            "reg_lambda":       1.0,
            "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, 18],
        "min_atr":          None,
        "trend_filter":     "sma_200",
        "target_horizon":   4,
        "objective":        (
            "Maximize risk-adjusted return (Sharpe/Calmar) on EUR/USD 15-min data. "
            "SMA triple-stack (20/50/200) provides trend context; supplementary "
            "momentum (RSI, MACD, Stochastic), volatility (ATR, BB), and "
            "mean-reversion (CCI, Williams %R) features give the XGBoost model a "
            "rich multi-regime signal set. XGBoost chosen for its ability to rank "
            "feature importance and handle non-linear interactions. Shallow trees "
            "(max_depth=4) with high n_estimators and low learning_rate reduce "
            "overfitting. 2:1 reward:risk (SL 0.5% / TP 1.0%) ensures positive "
            "expectancy even at moderate win rates. Session filter 06-18 UTC "
            "concentrates trades in liquid London/NY overlap."
        ),
        "notes":            (
            "trend_filter=sma_200 aligns trades with the dominant trend — longs "
            "only above the 200-SMA, shorts only below — acting as a regime gate "
            "to suppress counter-trend noise. signal_threshold=0.55 slightly above "
            "0.50 to reduce false positives without starving signal count. "
            "colsample_bytree=0.7 introduces randomisation across features to "
            "decorrelate trees and improve generalisation on forex data."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 01:54:56
# 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/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):
    # ── Bollinger Bands (20, 2) ──────────────────────────────────────────────
    bb_period = 20
    bb_std    = 2.0
    bb_mid    = close.rolling(bb_period).mean()
    bb_std_   = close.rolling(bb_period).std(ddof=0)
    bb_upper  = bb_mid + bb_std * bb_std_
    bb_lower  = bb_mid - bb_std * 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
    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()
    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"]

    # ── ATR (14) — for normalised volatility / min_atr filter ────────────────
    atr_period = 14
    tr = pd.concat([
        high - low,
        (high - close.shift(1)).abs(),
        (low  - close.shift(1)).abs()
    ], axis=1).max(axis=1)
    atr = tr.ewm(com=atr_period - 1, min_periods=atr_period).mean()
    df["atr"]  = atr
    df["natr"] = atr / close          # normalised ATR used by min_atr filter

    # ── Price momentum / rate-of-change ──────────────────────────────────────
    df["roc_4"]  = close.pct_change(4)    # 1-hour momentum on 15-min bars
    df["roc_8"]  = close.pct_change(8)    # 2-hour momentum
    df["roc_16"] = close.pct_change(16)   # 4-hour momentum

    # ── EMA trend context ─────────────────────────────────────────────────────
    df["ema_20"]  = close.ewm(span=20,  adjust=False).mean()
    df["ema_50"]  = close.ewm(span=50,  adjust=False).mean()
    df["ema_100"] = close.ewm(span=100, adjust=False).mean()
    df["sma_50"]  = close.rolling(50).mean()   # used by trend_filter

    df["ema_cross_20_50"]  = df["ema_20"]  - df["ema_50"]
    df["ema_cross_50_100"] = df["ema_50"]  - df["ema_100"]
    df["close_vs_ema20"]   = (close - df["ema_20"]) / df["ema_20"]

    # ── Candlestick body / wick features ─────────────────────────────────────
    df["body"]       = (close - open_).abs()
    df["candle_dir"] = np.where(close >= open_, 1.0, -1.0)
    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
    df["body_ratio"] = df["body"] / (high - low).replace(0, np.nan)

    # ── Volume-proxy: realised range rolling stats ────────────────────────────
    df["hl_range"]      = high - low
    df["hl_range_ma8"]  = df["hl_range"].rolling(8).mean()
    df["hl_range_ratio"]= df["hl_range"] / df["hl_range_ma8"]

    # ── RSI derived signals ───────────────────────────────────────────────────
    df["rsi_overbought"]  = np.where(df["rsi"] > 70, 1.0, 0.0)
    df["rsi_oversold"]    = np.where(df["rsi"] < 30, 1.0, 0.0)
    df["rsi_momentum"]    = df["rsi"].diff(4)

    # ── Stochastic derived signals ────────────────────────────────────────────
    df["stoch_overbought"] = np.where(df["stoch_k"] > 80, 1.0, 0.0)
    df["stoch_oversold"]   = np.where(df["stoch_k"] < 20, 1.0, 0.0)

    # ── BB squeeze: width vs rolling mean of width ────────────────────────────
    df["bb_width_ma20"]  = df["bb_width"].rolling(20).mean()
    df["bb_squeeze"]     = np.where(df["bb_width"] < df["bb_width_ma20"], 1.0, 0.0)

    # ── Interaction features ──────────────────────────────────────────────────
    df["rsi_bb_pct"]      = df["rsi"] * df["bb_pct"]
    df["stoch_k_bb_pct"]  = df["stoch_k"] * df["bb_pct"]
    df["rsi_stoch_diff"]  = df["rsi"] - df["stoch_k"]

    # ── Lagged features (avoids look-ahead) ──────────────────────────────────
    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"roc4_lag{lag}"]    = df["roc_4"].shift(lag)

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


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "NZD/USD Stoch+BB+RSI Gradient Boosting Mean-Revert",
        "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": 0.56,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [7, 20],
        "min_atr":          0.0002,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective": (
            "Maximise risk-adjusted return (Sharpe / Calmar) on NZD/USD 15-min. "
            "GradientBoostingClassifier selected for its strong generalisation on "
            "structured tabular data with noisy financial features. n_estimators=400 "
            "with early stopping (n_iter_no_change=30) prevents overfitting. "
            "max_depth=4 keeps trees shallow to reduce variance. subsample=0.8 + "
            "max_features=sqrt add stochasticity for robustness. SL 0.5% / TP 1.0% "
            "gives a minimum 2:1 reward-risk ratio. Session filter 07-20 UTC covers "
            "Sydney open through NY overlap, maximising NZD/USD liquidity. "
            "Reverse on opposite signal keeps the model continuously positioned in "
            "the highest-confidence direction. min_atr filter avoids flat/illiquid "
            "periods where the model edges degrade."
        ),
        "notes": (
            "Features: Bollinger Bands (20,2) width & %B, RSI(14), Stochastic K/D "
            "(14,3), ATR(14)/NATR, EMA cross (20/50/100), SMA50 trend context, "
            "price ROC (4/8/16 bars), candlestick body/wick ratios, HL range "
            "normalisation, BB squeeze flag, RSI/Stoch overbought-oversold flags, "
            "interaction terms (RSI*%B, StochK*%B), and 4 lags each of RSI, StochK, "
            "%B and ROC4. Threshold 0.56 slightly above 0.50 to filter marginal "
            "signals without sacrificing too many trades."
        ),
    }