Community Scripts

# ╔══════════════════════════════════════════════════════════════╗
# ║                  STRATEGY REQUEST LOG                       ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated        : 2026-09-06 15:14:19
# Model            : Logistic Regression
# Feature Eng.     : RSI 14, MACD (12,26,9), BB (20,2.0), Stochastic (14,3), ATR 14, ADX 14, EMA 9/21 cross, Candle structure, Return lags 1-3, Session/time features
# Signal / Entry   : Broad indicator set; gradient-boosted model learns the entry rule
# Optimization     : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt        : Stop loss 15 pips, Take profit 30 pips
# Risk Filter      : —
# ══════════════════════════════════════════════════════════════

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

DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/AUDUSD_5min.parquet"
START_DATE = "2026-07-28"
END_DATE   = "2026-09-06"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 15.0
TAKE_PROFIT = 30.0
AUX_FEEDS = []

# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
    # --- RSI(14) with zone flags and slope ---
    _d = close.diff()
    _g = _d.clip(lower=0).ewm(com=13, min_periods=14, adjust=False).mean()
    _l = (-_d.clip(upper=0)).ewm(com=13, min_periods=14, adjust=False).mean()
    _rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
    df["rsi_14"] = _rsi
    df["rsi_14_os"] = (_rsi < 30).astype(float)
    df["rsi_14_ob"] = (_rsi > 70).astype(float)
    df["rsi_14_slope"] = _rsi.diff(2)
    # --- MACD(12,26,9) ---
    _m = close.ewm(span=12, adjust=False).mean() - close.ewm(span=26, adjust=False).mean()
    _sig = _m.ewm(span=9, adjust=False).mean()
    df["macd_12_26"] = _m / close * 1e4
    df["macd_12_26_sig"] = _sig / close * 1e4
    df["macd_12_26_hist"] = (_m - _sig) / close * 1e4
    df["macd_12_26_hist_chg"] = df["macd_12_26_hist"].diff(1)
    # --- Bollinger Bands(20,2.0) ---
    _mid = close.rolling(20).mean()
    _sd = close.rolling(20).std()
    df["bb_20_2p0_pctb"] = (close - (_mid - 2.0 * _sd)) / (2 * 2.0 * _sd + 1e-10)
    df["bb_20_2p0_width"] = (2 * 2.0 * _sd) / (_mid + 1e-10)
    df["bb_20_2p0_width_chg"] = df["bb_20_2p0_width"].pct_change(3)
    # --- Stochastic(14,3) ---
    _ll = low.rolling(14).min()
    _hh = high.rolling(14).max()
    _k = 100.0 * (close - _ll) / (_hh - _ll + 1e-10)
    df["stoch_k_14"] = _k
    df["stoch_d_14"] = _k.rolling(3).mean()
    df["stoch_14_diff"] = df["stoch_k_14"] - df["stoch_d_14"]
    # --- ATR(14) normalised ---
    _tr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
    _atr = _tr.ewm(alpha=1.0/14, adjust=False).mean()
    df["atr_14_pct"] = _atr / close
    df["atr_14_ratio"] = _atr / (_atr.rolling(56).mean() + 1e-12)
    # --- ADX(14) with +DI/-DI ---
    _up = high.diff()
    _dn = -low.diff()
    _pdm = pd.Series(np.where((_up > _dn) & (_up > 0), _up, 0.0), index=df.index)
    _ndm = pd.Series(np.where((_dn > _up) & (_dn > 0), _dn, 0.0), index=df.index)
    _tr2 = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
    _atr2 = _tr2.ewm(alpha=1.0/14, adjust=False).mean()
    _pdi = 100.0 * _pdm.ewm(alpha=1.0/14, adjust=False).mean() / (_atr2 + 1e-12)
    _ndi = 100.0 * _ndm.ewm(alpha=1.0/14, adjust=False).mean() / (_atr2 + 1e-12)
    _dx = 100.0 * (_pdi - _ndi).abs() / (_pdi + _ndi + 1e-12)
    df["adx_14"] = _dx.ewm(alpha=1.0/14, adjust=False).mean()
    df["di_diff_14"] = _pdi - _ndi
    # --- EMA 9/21 crossover ---
    _ea = close.ewm(span=9, adjust=False).mean()
    _eb = close.ewm(span=21, adjust=False).mean()
    df["ema_9_21_diff"] = (_ea - _eb) / close * 1e4
    df["ema_9_21_diff_chg"] = df["ema_9_21_diff"].diff(1)
    df["ema_9_21_cross_up"] = ((_ea > _eb) & (_ea.shift(1) <= _eb.shift(1))).astype(float)
    df["ema_9_21_cross_dn"] = ((_ea < _eb) & (_ea.shift(1) >= _eb.shift(1))).astype(float)
    df["close_vs_ema_9"] = close / _ea - 1.0
    # --- Candle structure ---
    _rng = (high - low) + 1e-12
    df["body"] = (close - open_) / _rng
    df["upper_wick"] = (high - np.maximum(close, open_)) / _rng
    df["lower_wick"] = (np.minimum(close, open_) - low) / _rng
    df["range_pct"] = _rng / close
    df["range_rel"] = _rng / (_rng.rolling(20).mean() + 1e-12)
    # --- Return lags 1..3 ---
    _r = close.pct_change() * 1e4
    for _i in range(1, 4):
        df[f"ret_lag_{_i}"] = _r.shift(_i - 1)
    df["ret_sum_3"] = _r.rolling(3).sum()
    # --- Time-of-day / day-of-week (UTC) ---
    _h = df.index.hour + df.index.minute / 60.0
    df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
    df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
    df["dow"] = df.index.dayofweek.astype(float)
    df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
    df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)

    # Fill indicator warm-up gaps without looking ahead
    df = df.ffill().fillna(0.0)
    return df


# SECTION 2 — STRATEGY CONFIG
def strategy_config():
    return {
        "title": "AUD/USD Multi-Indicator Logistic Regression 5min",
        "model_type": "LogisticRegression",
        "model_params": {
                "C": 1.0,
                "max_iter": 2000,
                "random_state": 42
        },
        "signal_threshold": 0.55,
        "direction": "long",
        "stop_loss": 15.0,
        "take_profit": 30.0,
        "risk_unit": "pips",
        "cooldown": 0,
        "max_positions": 1,
        "on_opposite": "reverse",
        "session_filter": None,
        "min_atr": None,
        "trend_filter": None,
        "target_horizon": 8,
        "objective": "Multi-Indicator strategy on AUD/USD 5min: Broad indicator set; gradient-boosted model learns the entry rule. Features: RSI 14, MACD (12,26,9), BB (20,2.0), Stochastic (14,3), ATR 14, ADX 14, EMA 9/21 cross, Candle structure, Return lags 1-3, Session/time features. Model: Logistic Regression. Label horizon 8 bars, confidence threshold 0.55, direction long. Risk: 15 pip stop / 30 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
        "notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
    }