Community Scripts

# ╔══════════════════════════════════════════════════════════════╗
# ║                  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."
        ),
    }