Community Scripts

# ╔══════════════════════════════════════════════════════════════╗
# ║                  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        : 2026-05-25 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 02:24:20
# 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/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):
    # ── 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=1)
    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"]  = 100.0 - (100.0 / (1.0 + rs))

    # ── Stochastic Oscillator (K=14, D=3) ────────────────────────────────────
    stoch_k = 14
    stoch_d = 3
    low_min  = low.rolling(stoch_k).min()
    high_max = high.rolling(stoch_k).max()
    k_raw    = 100.0 * (close - low_min) / (high_max - low_min).replace(0, np.nan)
    df["stoch_k"] = k_raw
    df["stoch_d"] = k_raw.rolling(stoch_d).mean()

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

    # ── Trend / Momentum features ─────────────────────────────────────────────
    df["sma_20"]  = close.rolling(20).mean()
    df["sma_50"]  = close.rolling(50).mean()
    df["sma_100"] = close.rolling(100).mean()

    df["price_vs_sma20"]  = (close - df["sma_20"])  / df["sma_20"]
    df["price_vs_sma50"]  = (close - df["sma_50"])  / df["sma_50"]
    df["price_vs_sma100"] = (close - df["sma_100"]) / df["sma_100"]
    df["sma20_vs_sma50"]  = (df["sma_20"] - df["sma_50"]) / df["sma_50"]

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

    # ── Rate-of-Change features ───────────────────────────────────────────────
    for p in [4, 8, 16]:
        df[f"roc_{p}"] = close.pct_change(p)

    # ── Volatility regime ────────────────────────────────────────────────────
    df["vol_8"]  = close.pct_change().rolling(8).std()
    df["vol_20"] = close.pct_change().rolling(20).std()
    df["vol_ratio"] = df["vol_8"] / df["vol_20"].replace(0, np.nan)

    # ── Candle body / shadow features ────────────────────────────────────────
    df["body"]        = (close - open_).abs()
    df["upper_shadow"] = high - pd.concat([close, open_], axis=1).max(axis=1)
    df["lower_shadow"] = pd.concat([close, open_], axis=1).min(axis=1) - low
    df["body_ratio"]  = df["body"] / (high - low).replace(0, np.nan)

    # ── RSI-derived features ──────────────────────────────────────────────────
    df["rsi_above_50"] = np.where(df["rsi"] > 50, 1, 0)
    df["rsi_overbought"] = np.where(df["rsi"] > 70, 1, 0)
    df["rsi_oversold"]   = np.where(df["rsi"] < 30, 1, 0)
    df["rsi_lag1"] = df["rsi"].shift(1)
    df["rsi_lag4"] = df["rsi"].shift(4)

    # ── Stochastic-derived features ───────────────────────────────────────────
    df["stoch_cross_up"]   = np.where((df["stoch_k"] > df["stoch_d"]) &
                                      (df["stoch_k"].shift(1) <= df["stoch_d"].shift(1)), 1, 0)
    df["stoch_cross_down"] = np.where((df["stoch_k"] < df["stoch_d"]) &
                                      (df["stoch_k"].shift(1) >= df["stoch_d"].shift(1)), 1, 0)
    df["stoch_oversold"]   = np.where(df["stoch_k"] < 20, 1, 0)
    df["stoch_overbought"] = np.where(df["stoch_k"] > 80, 1, 0)

    # ── BB-derived features ───────────────────────────────────────────────────
    df["bb_squeeze"] = np.where(df["bb_width"] < df["bb_width"].rolling(50).quantile(0.20), 1, 0)
    df["above_bb_upper"] = np.where(close > bb_upper, 1, 0)
    df["below_bb_lower"] = np.where(close < bb_lower, 1, 0)
    df["bb_pct_lag1"]    = df["bb_pct"].shift(1)
    df["bb_pct_lag4"]    = df["bb_pct"].shift(4)

    # ── Session hour (UTC) ────────────────────────────────────────────────────
    df["hour_utc"] = df.index.hour if hasattr(df.index, "hour") else 0

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

# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "AUD/USD Stochastic BB Mean-Reversion (GBM)",
        "model_type":       "GradientBoostingClassifier",
        "model_params":     {
            "n_estimators":      400,
            "max_depth":         4,
            "learning_rate":     0.04,
            "subsample":         0.80,
            "min_samples_leaf":  20,
            "max_features":      "sqrt",
            "n_iter_no_change":  30,
            "validation_fraction": 0.10,
            "tol":               1e-4,
        },
        "signal_threshold": 0.55,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   None,
        "min_atr":          None,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective": (
            "Maximize risk-adjusted return (Sharpe/Calmar) on AUD/USD 15-min. "
            "GradientBoostingClassifier with moderate depth and learning rate chosen "
            "to balance bias-variance. 2:1 reward-to-risk (SL=0.5%, TP=1.0%). "
            "Stochastic crossovers, BB mean-reversion, and RSI regime signals "
            "form the core feature set; MACD, volatility, and candle features add "
            "context. Early stopping (n_iter_no_change=30) prevents overfitting."
        ),
        "notes": (
            "Features: Bollinger Bands (20,2) width/pct, RSI(14) with lag/regime flags, "
            "Stochastic(14,3) K/D with crossover detection, ATR/NATR volatility, MACD "
            "histogram, short/medium SMAs, ROC(4/8/16), volatility ratio, candle body "
            "ratios, and UTC session hour. No session or trend filter to allow full "
            "mean-reversion opportunities across all sessions."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 01:44:26
# Model            : XGBoost
# 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/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):
    # ── 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
    df["bb_width"] = (bb_upper - bb_lower) / bb_mid
    # guard against zero range
    bb_range = bb_upper - bb_lower
    df["bb_pct"]   = np.where(bb_range != 0, (close - bb_lower) / bb_range, 0.5)

    # ── 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, min_periods=atr_period, adjust=False).mean()
    df["atr"]  = atr
    df["natr"] = np.where(close != 0, atr / close, 0.0)

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

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

    # ── EMA trend features ───────────────────────────────────────────────────
    ema_20  = close.ewm(span=20, adjust=False).mean()
    ema_50  = close.ewm(span=50, adjust=False).mean()
    ema_200 = close.ewm(span=200, adjust=False).mean()
    df["ema_20"]        = ema_20
    df["ema_50"]        = ema_50
    df["ema_200"]       = ema_200
    df["close_vs_ema20"]  = (close - ema_20) / ema_20
    df["close_vs_ema50"]  = (close - ema_50) / ema_50
    df["ema20_vs_ema50"]  = (ema_20 - ema_50) / ema_50
    df["ema50_vs_ema200"] = (ema_50 - ema_200) / ema_200

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

    # ── Rolling volatility ────────────────────────────────────────────────────
    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)

    # ── Stochastic %K / %D (14, 3) ───────────────────────────────────────────
    low_14  = low.rolling(14).min()
    high_14 = high.rolling(14).max()
    stoch_range = high_14 - low_14
    stoch_k = np.where(stoch_range != 0,
                       100 * (close - low_14) / stoch_range, 50.0)
    df["stoch_k"] = stoch_k
    df["stoch_d"] = pd.Series(stoch_k, index=close.index).rolling(3).mean()

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

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

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

    # ── Volume proxy (if volume column exists) ───────────────────────────────
    if "volume" in df.columns:
        vol_ma = df["volume"].rolling(20).mean()
        df["volume_ratio"] = np.where(vol_ma != 0,
                                      df["volume"] / vol_ma, 1.0)

    # ── Lagged features (1-bar lag to avoid lookahead) ───────────────────────
    for feat in ["bb_pct", "rsi_14", "macd_hist", "natr", "stoch_k"]:
        df[f"{feat}_lag1"] = df[feat].shift(1)
        df[f"{feat}_lag2"] = df[feat].shift(2)

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

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "AUD/USD Bollinger + ATR Mean-Rev (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":              0.10,
            "reg_alpha":          0.10,
            "reg_lambda":         1.50,
            "objective":          "binary:logistic",
            "random_state":       42,
            "n_jobs":             -1,
        },
        "signal_threshold": 0.56,
        "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). "
            "XGBoost with moderate depth and heavy regularisation "
            "(gamma, alpha, lambda) prevents overfit on AUD/USD 15-min data. "
            "Bollinger Bands capture mean-reversion; ATR normalises volatility; "
            "RSI + MACD confirm momentum; 2:1 TP:SL ratio supports positive expectancy."
        ),
        "notes": (
            "Features: BB (20,2) width/pct, ATR-14/NATR, RSI-14, MACD histogram, "
            "EMA 20/50/200 spreads, Stochastic %K/%D, candle-body ratios, "
            "ROC at multiple horizons, volatility ratio, BB squeeze flag, "
            "lagged versions of key features. "
            "Threshold 0.56 filters marginal signals, improving precision. "
            "target_horizon=4 (1 hour) balances signal frequency vs. noise."
        ),
    }

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    return df

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

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 03:16:00
# Model            : Gradient Boosting
# 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/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):
    # ── 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: ema_50 vs ema_200
    df["ema_cross"] = df["ema_50"] - df["ema_200"]

    # Cross direction: +1 when ema_50 > ema_200, -1 otherwise
    df["ema_cross_sign"] = np.where(df["ema_cross"] > 0, 1.0, -1.0)

    # Cross event: 1 when cross just happened (sign flip)
    prev_cross = df["ema_cross"].shift(1)
    df["ema_cross_event"] = np.where(
        (df["ema_cross"] * prev_cross) < 0, 1.0, 0.0
    )

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

    # ── RSI 14 ──────────────────────────────────────────────────────────────
    delta  = close.diff()
    gain   = delta.clip(lower=0)
    loss   = (-delta).clip(lower=0)
    avg_gain = gain.ewm(span=14, adjust=False).mean()
    avg_loss = loss.ewm(span=14, adjust=False).mean()
    rs  = avg_gain / (avg_loss + 1e-10)
    rsi = 100 - (100 / (1 + rs))
    df["rsi_14"] = rsi
    df["rsi_norm"] = (rsi - 50) / 50   # centred and scaled

    # ── MACD ────────────────────────────────────────────────────────────────
    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_norm"]    = macd / close
    df["macd_hist_norm"] = (macd - signal) / 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
    bb_width = (bb_upper - bb_lower) / (sma_20 + 1e-10)
    bb_pos   = (close - bb_lower) / (bb_upper - bb_lower + 1e-10)
    df["bb_width"] = bb_width
    df["bb_pos"]   = bb_pos

    # ── Momentum & Rate-of-Change ────────────────────────────────────────────
    df["mom_4"]  = close.pct_change(4)
    df["mom_8"]  = close.pct_change(8)
    df["mom_16"] = close.pct_change(16)

    # ── Rolling volatility (realised vol over 20 bars) ──────────────────────
    log_ret = np.log(close / close.shift(1))
    df["rvol_20"] = log_ret.rolling(20).std()

    # ── Stochastic Oscillator (14) ───────────────────────────────────────────
    low_14  = low.rolling(14).min()
    high_14 = high.rolling(14).max()
    stoch_k = 100 * (close - low_14) / (high_14 - low_14 + 1e-10)
    stoch_d = stoch_k.rolling(3).mean()
    df["stoch_k"] = stoch_k
    df["stoch_d"] = stoch_d
    df["stoch_diff"] = stoch_k - stoch_d

    # ── Candle body / range features ────────────────────────────────────────
    df["body"]       = (close - open_).abs() / (high - low + 1e-10)
    df["upper_wick"] = (high - close.clip(lower=open_)) / (high - low + 1e-10)
    df["lower_wick"] = (close.clip(upper=open_) - low)  / (high - low + 1e-10)
    df["bar_dir"]    = np.where(close > open_, 1.0, -1.0)

    # ── Price position relative to EMAs ─────────────────────────────────────
    df["close_vs_ema50_sign"]  = np.where(close > ema_50,  1.0, -1.0)
    df["close_vs_ema200_sign"] = np.where(close > ema_200, 1.0, -1.0)

    # ── Lagged features (1-bar and 2-bar lags on key signals) ───────────────
    for col in ["rsi_norm", "macd_hist_norm", "mom_4", "ema_cross", "natr", "bb_pos"]:
        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 EMA Cross + ATR Gradient Boosting",
        "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,
        },
        "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 (Sharpe / Calmar). "
            "GradientBoostingClassifier with moderate depth (4) and low learning rate (0.03) "
            "to reduce overfitting on 15-min NZD/USD. SL=0.5%, TP=1.0% gives 1:2 RR. "
            "EMA 50/200 cross is the primary trend feature; ATR normalises volatility context. "
            "Supplementary RSI, MACD, Bollinger, Stochastic and candle-body features capture "
            "momentum and mean-reversion signals. Early stopping via n_iter_no_change guards "
            "against overfit on the training partition."
        ),
        "notes": (
            "target_horizon=4 (1 hour) matches typical intraday swing on NZD/USD. "
            "reverse on opposite signal keeps the model responsive during trending regimes. "
            "No session filter applied — NZD/USD has reasonable liquidity around the clock. "
            "min_samples_leaf=20 and subsample=0.8 add regularisation without grid search."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 02:32:18
# 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      : —
# ══════════════════════════════════════════════════════════════

# ============================================================
# AUDUSD 15-min XGBoost Strategy
# SMA + RSI + MACD + Bollinger Bands + ATR Feature Set
# Optimized for Risk-Adjusted Return
# ============================================================

# 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):

    # ── 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, 2.0) ───────────────────────────────────────────
    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
    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"]   = (close - bb_lower) / bb_range

    # ── RSI 14 ──────────────────────────────────────────────────────────────
    delta  = close.diff()
    gain   = delta.clip(lower=0)
    loss   = (-delta).clip(lower=0)
    avg_gain = gain.ewm(com=13, min_periods=14).mean()
    avg_loss = loss.ewm(com=13, min_periods=14).mean()
    rs         = avg_gain / avg_loss.replace(0, np.nan)
    df["rsi_14"] = 100.0 - (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 + Normalised ATR ─────────────────────────────────────────────
    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).mean()
    df["atr_14"] = atr
    df["natr"]   = atr / close

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

    # ── 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["candle_dir"]  = np.where(close >= open_, 1.0, -1.0)

    # ── Volume (if present) ─────────────────────────────────────────────────
    if "volume" in df.columns:
        vol_ma = df["volume"].rolling(20).mean()
        df["vol_ratio"] = df["volume"] / vol_ma.replace(0, np.nan)

    # ── Lagged RSI & MACD histogram ─────────────────────────────────────────
    for lag in [1, 2, 3]:
        df[f"rsi_14_lag{lag}"]   = df["rsi_14"].shift(lag)
        df[f"macd_hist_lag{lag}"] = df["macd_hist"].shift(lag)

    # ── RSI overbought / oversold zones ─────────────────────────────────────
    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_up"] = np.where((df["rsi_14"] > 50) & (df["rsi_14"] <= 70), 1.0, 0.0)
    df["rsi_mid_dn"] = np.where((df["rsi_14"] >= 30) & (df["rsi_14"] < 50), 1.0, 0.0)

    # ── MACD cross signals ───────────────────────────────────────────────────
    df["macd_cross_up"] = np.where(
        (df["macd_line"] > df["macd_signal"]) &
        (df["macd_line"].shift(1) <= df["macd_signal"].shift(1)),
        1.0, 0.0
    )
    df["macd_cross_dn"] = np.where(
        (df["macd_line"] < df["macd_signal"]) &
        (df["macd_line"].shift(1) >= df["macd_signal"].shift(1)),
        1.0, 0.0
    )

    # ── Price position relative to SMA alignment ────────────────────────────
    df["trend_aligned_bull"] = np.where(
        (close > df["sma_20"]) & (df["sma_20"] > df["sma_50"]) & (df["sma_50"] > df["sma_200"]),
        1.0, 0.0
    )
    df["trend_aligned_bear"] = np.where(
        (close < df["sma_20"]) & (df["sma_20"] < df["sma_50"]) & (df["sma_50"] < df["sma_200"]),
        1.0, 0.0
    )

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

    # ── Rolling close statistics ─────────────────────────────────────────────
    df["close_zscore_20"] = (close - close.rolling(20).mean()) / close.rolling(20).std(ddof=0)
    df["close_zscore_50"] = (close - close.rolling(50).mean()) / close.rolling(50).std(ddof=0)

    # ── Volatility regime ────────────────────────────────────────────────────
    natr_ma = df["natr"].rolling(20).mean()
    df["vol_regime_high"] = np.where(df["natr"] > natr_ma, 1.0, 0.0)

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

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "AUD/USD XGBoost SMA+RSI+MACD+BB Momentum",
        "model_type":       "XGBClassifier",
        "model_params":     {
            "n_estimators":       500,
            "max_depth":          4,
            "learning_rate":      0.04,
            "subsample":          0.75,
            "colsample_bytree":   0.70,
            "min_child_weight":   5,
            "gamma":              0.1,
            "reg_alpha":          0.1,
            "reg_lambda":         1.5,
            "scale_pos_weight":   1.0,
            "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":        (
            "Maximize risk-adjusted return (Sharpe/Calmar) on AUD/USD 15-min. "
            "XGBoost with depth-4 trees and conservative regularization (reg_lambda=1.5, "
            "min_child_weight=5) to reduce overfitting on FX data. "
            "2:1 RR (SL=0.5%, TP=1.0%) ensures positive expectancy with ~40%+ win rate. "
            "Subsample + colsample add stochastic diversity. 500 estimators with lr=0.04 "
            "balances bias-variance. Threshold 0.55 filters marginal signals."
        ),
        "notes":            (
            "Features: SMA(20/50/200) with distances, Bollinger Bands width+pct, RSI-14 "
            "with zone flags, MACD histogram + crosses, ATR-14 + NATR, momentum ROC(1/4/8/16), "
            "candle body/wick ratios, trend alignment flags, BB squeeze, z-scores, vol regime. "
            "Reverse on opposite signal to capture trend reversals. Session filter disabled "
            "to capture AUD/USD Asian + London + NY sessions. Target horizon = 4 bars (1 hour)."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  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        : 2026-05-06 02:54:13
# Model            : Gradient Boosting
# Feature Eng.     : EMA (9,21), RSI 14 + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

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

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/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):
    # ── EMA 9 and EMA 21 (required) ──────────────────────────────────────────
    ema_9  = close.ewm(span=9,  adjust=False).mean()
    ema_21 = close.ewm(span=21, adjust=False).mean()

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

    # EMA crossover signal: positive when fast > slow
    df["ema_cross"] = ema_9 - ema_21

    # Rate of change of the crossover (momentum of the cross)
    df["ema_cross_roc"] = df["ema_cross"].diff(3)

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

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

    # RSI momentum (1-bar diff of RSI)
    df["rsi_diff"] = df["rsi_14"].diff(1)

    # RSI overbought / oversold flags (np.where, no pd.cut)
    df["rsi_ob"] = np.where(df["rsi_14"] > 70, 1, 0)
    df["rsi_os"] = np.where(df["rsi_14"] < 30, 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(com=13, adjust=False).mean()
    df["atr_14"]  = atr_14
    df["natr_14"] = atr_14 / close  # normalised ATR

    # ── MACD-style fast/slow difference (12/26 EMA) ─────────────────────────
    ema_12 = close.ewm(span=12, adjust=False).mean()
    ema_26 = close.ewm(span=26, adjust=False).mean()
    macd_line   = ema_12 - ema_26
    macd_signal = macd_line.ewm(span=9, adjust=False).mean()
    df["macd_line"]   = macd_line / close
    df["macd_signal"] = macd_signal / close
    df["macd_hist"]   = (macd_line - macd_signal) / close

    # ── Bollinger Bands (20, 2) ───────────────────────────────────────────────
    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
    bb_bw   = (bb_up - bb_lo) / bb_mid          # bandwidth
    bb_pct  = (close - bb_lo) / (bb_up - bb_lo) # %B position
    df["bb_bandwidth"] = bb_bw
    df["bb_pct"]       = bb_pct

    # ── 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_diff"] = stoch_k - stoch_d

    # ── Price momentum (returns over multiple horizons) ───────────────────────
    df["ret_1"]  = close.pct_change(1)
    df["ret_3"]  = close.pct_change(3)
    df["ret_6"]  = close.pct_change(6)
    df["ret_12"] = close.pct_change(12)

    # ── Candle 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["body_direction"]   = np.sign(close - open_)

    # ── Volume proxy: volatility-based (OHLC spread) ─────────────────────────
    df["hl_spread"] = (high - low) / close

    # ── Rolling volatility (std of returns) ──────────────────────────────────
    df["vol_6"]  = df["ret_1"].rolling(6).std()
    df["vol_24"] = df["ret_1"].rolling(24).std()

    # ── Z-score of close relative to 20-bar rolling mean ─────────────────────
    roll_mean_20 = close.rolling(20).mean()
    roll_std_20  = close.rolling(20).std()
    df["zscore_20"] = (close - roll_mean_20) / roll_std_20.replace(0, np.nan)

    # ── Trend strength: R² of close over 20 bars ─────────────────────────────
    x = np.arange(20)
    x_demeaned = x - x.mean()
    ss_x = (x_demeaned ** 2).sum()

    def rolling_r2(series, window=20):
        arr = series.values
        n   = len(arr)
        out = np.full(n, np.nan)
        for i in range(window - 1, n):
            y = arr[i - window + 1: i + 1]
            if np.any(np.isnan(y)):
                continue
            y_m = y - y.mean()
            slope = np.dot(x_demeaned, y_m) / ss_x
            y_hat = slope * x_demeaned + y.mean()
            ss_res = ((y - y_hat) ** 2).sum()
            ss_tot = ((y - y.mean()) ** 2).sum()
            out[i] = 1 - ss_res / ss_tot if ss_tot > 0 else 0.0
        return out

    df["trend_r2_20"] = rolling_r2(close, 20)

    # ── SMA 50 (for trend filter reference; also used as feature) ─────────────
    sma_50 = close.rolling(50).mean()
    df["sma_50"]       = sma_50
    df["close_vs_sma50"] = (close - sma_50) / sma_50

    # ── Higher-timeframe EMA proxy (4-bar resample = 1h equivalent) ──────────
    ema_4h = close.ewm(span=4 * 21, adjust=False).mean()
    df["dm_ema_4h"] = (close - ema_4h) / ema_4h

    # ── Cross confirmation: EMA cross direction × RSI regime ─────────────────
    df["cross_x_rsi"] = np.sign(df["ema_cross"]) * df["rsi_norm"]

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

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "NZD/USD EMA Cross (9/21) + RSI Gradient Boost",
        "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":  25,
            "tol":               1e-4,
        },
        "signal_threshold": 0.55,
        "direction":        "both",
        "stop_loss":        0.005,
        "take_profit":      0.010,
        "cooldown":         0,
        "max_positions":    1,
        "on_opposite":      "reverse",
        "session_filter":   [0, 23],
        "min_atr":          None,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective":        (
            "Maximise risk-adjusted return (Sharpe) on NZD/USD 15-min data. "
            "GradientBoostingClassifier chosen for its strong out-of-box performance on "
            "tabular financial data, built-in regularisation via subsample/max_features, "
            "and early-stopping via n_iter_no_change. Depth-4 trees with 400 estimators "
            "and lr=0.04 balance bias-variance. SL=0.5% / TP=1.0% gives 1:2 R:R. "
            "4-bar horizon (~1 hour) aligns with EMA-cross momentum persistence. "
            "Threshold 0.55 filters marginal signals while preserving trade frequency."
        ),
        "notes":            (
            "Features: EMA 9/21 cross + distances, RSI 14 with OB/OS flags, MACD histogram, "
            "Bollinger %B + bandwidth, Stochastic K/D, multi-horizon returns, candle body "
            "ratios, rolling volatility, 20-bar z-score, trend R² and SMA50 distance. "
            "No session filter applied — NZD/USD has meaningful liquidity across Asian + "
            "London sessions. Reverse on opposite signal for continuous market exposure."
        ),
    }

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 01:51:31
# Model            : XGBoost
# Feature Eng.     : BB (20,2.0), RSI 14, Stochastic (14,3) + Auto-add features: ON
# Signal / Entry   : Enter when model confidence > threshold; exit on opposite signal or SL/TP
# Optimization     : Maximize risk-adjusted return
# Risk Mgmt        : Stop loss 0.5%, Take profit 1.0%
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

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

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/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):

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

    df["bb_mid"]   = bb_mid
    df["bb_upper"] = bb_upper
    df["bb_lower"] = bb_lower
    df["bb_width"] = (bb_upper - bb_lower) / bb_mid
    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()
    denom          = (highest_high - lowest_low).replace(0, np.nan)
    df["stoch_k"]  = 100 * (close - lowest_low) / denom
    df["stoch_d"]  = df["stoch_k"].rolling(stoch_d_period).mean()
    df["stoch_kd_diff"] = df["stoch_k"] - df["stoch_d"]

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

    # RSI overbought / oversold zone flags
    df["rsi_ob"]   = np.where(df["rsi"] > 70, 1, 0)
    df["rsi_os"]   = np.where(df["rsi"] < 30, 1, 0)
    df["rsi_mid"]  = df["rsi"] - 50.0

    # Stochastic overbought / oversold zone flags
    df["stoch_ob"] = np.where(df["stoch_k"] > 80, 1, 0)
    df["stoch_os"] = np.where(df["stoch_k"] < 20, 1, 0)

    # BB position regime: price relative to bands
    df["price_above_bb_upper"] = np.where(close > bb_upper, 1, 0)
    df["price_below_bb_lower"] = np.where(close < bb_lower, 1, 0)
    df["price_vs_bb_mid"]      = close - bb_mid

    # ATR-based volatility (14-bar)
    atr_period = 14
    tr = pd.concat([
        high - low,
        (high - close.shift(1)).abs(),
        (low  - close.shift(1)).abs()
    ], axis=1).max(axis=1)
    df["atr14"]  = tr.ewm(com=atr_period - 1, min_periods=atr_period).mean()
    df["natr14"] = df["atr14"] / close

    # SMA trend context
    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"]
    df["price_vs_sma50"]  = (close - df["sma_50"]) / df["sma_50"]
    df["sma20_vs_sma50"]  = (df["sma_20"] - df["sma_50"]) / df["sma_50"]

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

    # MACD-style (EMA 12 - EMA 26)
    ema12        = close.ewm(span=12, min_periods=12).mean()
    ema26        = close.ewm(span=26, min_periods=26).mean()
    macd_line    = ema12 - ema26
    macd_signal  = macd_line.ewm(span=9, min_periods=9).mean()
    df["macd"]        = macd_line
    df["macd_signal"] = macd_signal
    df["macd_hist"]   = macd_line - macd_signal

    # Candle body / wick ratios
    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["bullish_bar"]  = np.where(close > open_, 1, 0)

    # Lagged RSI / Stoch features (1 and 2 bars back)
    df["rsi_lag1"]     = df["rsi"].shift(1)
    df["rsi_lag2"]     = df["rsi"].shift(2)
    df["stoch_k_lag1"] = df["stoch_k"].shift(1)
    df["bb_pct_lag1"]  = df["bb_pct"].shift(1)

    # RSI slope
    df["rsi_slope"] = df["rsi"] - df["rsi"].shift(3)

    # Stoch K crossing D (momentum signal)
    df["stoch_cross_up"]   = np.where((df["stoch_k"] > df["stoch_d"]) &
                                       (df["stoch_k"].shift(1) <= df["stoch_d"].shift(1)), 1, 0)
    df["stoch_cross_down"] = np.where((df["stoch_k"] < df["stoch_d"]) &
                                       (df["stoch_k"].shift(1) >= df["stoch_d"].shift(1)), 1, 0)

    # Volume (if present)
    if "volume" in df.columns:
        vol_ma = df["volume"].rolling(20).mean()
        df["vol_ratio"] = df["volume"] / vol_ma.replace(0, np.nan)
    else:
        df["vol_ratio"] = 1.0

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

    return df

# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "AUD/USD Stoch+BB+RSI Mean-Reversion 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":   5,
            "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":   [0, 23],
        "min_atr":          None,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective": (
            "Maximize risk-adjusted return (Sharpe / Calmar). "
            "XGBoost chosen for its ability to capture non-linear interactions "
            "between Stochastic, Bollinger Bands, and RSI regimes. "
            "Shallow trees (max_depth=4) + high regularisation (reg_lambda=1.5, gamma=0.15) "
            "prevent overfitting on 15-min FX data. "
            "2:1 TP:SL ratio (1.0% / 0.5%) improves expectancy per trade. "
            "Reverse on opposite signal minimises flat time and captures regime flips."
        ),
        "notes": (
            "Features include BB width/pct, RSI(14) with overbought/oversold flags, "
            "Stochastic K/D crossovers, MACD histogram, ATR volatility, SMA trend context, "
            "candle body ratios, lagged indicators, and momentum ROC. "
            "signal_threshold=0.55 balances precision vs recall on directional calls. "
            "session_filter covers full 24h to capture Asia + London + NY sessions for AUD/USD."
        ),
    }

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-05-06 01:37:19
# 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/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):

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

    # SMA cross signals
    df["sma_20_50_cross"] = np.where(df["sma_20"] > df["sma_50"], 1.0, -1.0)
    df["sma_50_200_cross"] = np.where(df["sma_50"] > df["sma_200"], 1.0, -1.0)

    # ── Bollinger Bands ───────────────────────────────────────────────────────
    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_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)

    # Bollinger Band squeeze: width vs its own 20-bar average
    df["bb_squeeze"] = df["bb_width"] / df["bb_width"].rolling(20).mean()

    # Price position relative to bands
    df["bb_above_upper"] = np.where(close > bb_upper, 1.0, 0.0)
    df["bb_below_lower"] = np.where(close < bb_lower, 1.0, 0.0)

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

    # RSI derived features
    df["rsi_norm"] = (df["rsi_14"] - 50) / 50          # centred & scaled
    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_slope"] = df["rsi_14"].diff(3)

    # RSI divergence proxy: price up but RSI down (5-bar)
    price_chg_5 = close.diff(5)
    rsi_chg_5 = df["rsi_14"].diff(5)
    df["rsi_bear_div"] = np.where((price_chg_5 > 0) & (rsi_chg_5 < 0), 1.0, 0.0)
    df["rsi_bull_div"] = np.where((price_chg_5 < 0) & (rsi_chg_5 > 0), 1.0, 0.0)

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

    # MACD normalised by close price
    df["macd_line_norm"] = macd_line / close
    df["macd_hist_norm"] = df["macd_hist"] / close

    # MACD histogram slope and sign change
    df["macd_hist_slope"] = df["macd_hist"].diff(2)
    df["macd_cross"] = np.where(macd_line > signal_line, 1.0, -1.0)

    # ── ATR ───────────────────────────────────────────────────────────────────
    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(alpha=1/14, adjust=False).mean()
    df["natr"]   = df["atr_14"] / close

    # ATR regime: current ATR vs 50-bar rolling mean
    df["atr_regime"] = df["atr_14"] / df["atr_14"].rolling(50).mean()

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

    # Candle body & shadow
    body = (close - open_).abs()
    total_range = (high - low).replace(0, np.nan)
    df["body_ratio"] = body / total_range
    df["candle_dir"] = np.where(close >= open_, 1.0, -1.0)
    upper_shadow = high - pd.concat([close, open_], axis=1).max(axis=1)
    lower_shadow = pd.concat([close, open_], axis=1).min(axis=1) - low
    df["upper_shadow_ratio"] = upper_shadow / total_range
    df["lower_shadow_ratio"] = lower_shadow / total_range

    # Rolling price z-score (mean reversion signal)
    for w in [20, 50]:
        roll_mean = close.rolling(w).mean()
        roll_std  = close.rolling(w).std().replace(0, np.nan)
        df[f"zscore_{w}"] = (close - roll_mean) / roll_std

    # Volume of volatility: rolling std of returns
    df["vol_10"] = close.pct_change().rolling(10).std()
    df["vol_20"] = close.pct_change().rolling(20).std()

    # Efficiency ratio: directional move / path length (20 bars)
    direction_move = (close - close.shift(20)).abs()
    path_length = close.diff().abs().rolling(20).sum().replace(0, np.nan)
    df["efficiency_ratio"] = direction_move / path_length

    # ── Interaction / Cross features ─────────────────────────────────────────
    # RSI × MACD hist — captures momentum agreement
    df["rsi_macd_agree"] = df["rsi_norm"] * df["macd_hist_norm"]

    # BB pct × RSI — oversold/overbought near bands
    df["bb_rsi_interact"] = df["bb_pct"] * df["rsi_norm"]

    # Trend strength: distance from SMA50 scaled by ATR
    df["trend_atr_50"] = df["dm_sma_50"] / df["natr"].replace(0, np.nan)

    # ── Session / Time features ───────────────────────────────────────────────
    if hasattr(df.index, "hour"):
        hour = df.index.hour
        df["hour_sin"] = np.sin(2 * np.pi * hour / 24)
        df["hour_cos"] = np.cos(2 * np.pi * hour / 24)
        # London session flag
        df["london_session"] = np.where((hour >= 7) & (hour < 16), 1.0, 0.0)
        # NY session flag
        df["ny_session"] = np.where((hour >= 13) & (hour < 21), 1.0, 0.0)

    if hasattr(df.index, "dayofweek"):
        dow = df.index.dayofweek
        df["dow_sin"] = np.sin(2 * np.pi * dow / 5)
        df["dow_cos"] = np.cos(2 * np.pi * dow / 5)

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

    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title":            "USD/CAD Momentum-Reversion Hybrid (XGBoost, v2)",
        "model_type":       "XGBClassifier",
        "model_params": {
            "n_estimators":     600,
            "max_depth":        4,
            "learning_rate":    0.03,
            "subsample":        0.75,
            "colsample_bytree": 0.70,
            "min_child_weight": 5,
            "gamma":            0.2,
            "reg_alpha":        0.5,
            "reg_lambda":       2.0,
            "objective":        "binary:logistic",
            "tree_method":      "hist",
            "n_jobs":           -1,
            "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":   [7, 21],
        "min_atr":          0.0002,
        "trend_filter":     None,
        "target_horizon":   4,
        "objective": (
            "Maximise risk-adjusted return (Sharpe/Calmar). "
            "Deeper ensemble (600 trees) with aggressive regularisation "
            "(reg_alpha=0.5, reg_lambda=2, gamma=0.2, min_child_weight=5) "
            "to prevent overfitting on 15-min USDCAD. "
            "Rich feature set adds z-scores, efficiency ratio, session dummies, "
            "RSI divergence, candle shape and cross-indicator interactions "
            "beyond the prior attempt's plain indicators. "
            "0.5% SL / 1.0% TP gives 1:2 R:R; session filter restricts to "
            "liquid London+NY overlap hours."
        ),
        "notes": (
            "Prior attempt used plain RSI/MACD/BB/ATR/SMA and scored PF=0.98. "
            "This version adds: rolling z-scores (20,50), efficiency ratio, "
            "candle body/shadow ratios, multi-horizon returns, ATR regime, "
            "BB squeeze, RSI divergence proxies, time-of-day sin/cos encoding, "
            "and interaction terms (rsi_macd_agree, bb_rsi_interact, trend_atr). "
            "Model regularised more heavily to combat the short date range. "
            "Signal threshold lifted slightly to 0.54 to reduce marginal trades."
        ),
    }

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    return df


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