{
  "features": {
    "ret_1": {
      "name": "Lag-1 Return (1-bar momentum)",
      "formula": "(close[t-1] - close[t-2]) / close[t-2]",
      "python": "df['close'].shift(1).pct_change()",
      "description": "Single bar percentage return, captures immediate price momentum for mean-reversion identification",
      "importance": 0.0493,
      "value_range": [-0.05, 0.05],
      "units": "fraction"
    },
    "ret_3": {
      "name": "3-Bar Return",
      "formula": "(close[t-1] - close[t-4]) / close[t-4]",
      "python": "df['close'].shift(1) / df['close'].shift(4) - 1",
      "description": "Cumulative return over 3 bars, smooths single-bar noise and confirms trends",
      "importance": 0.0495,
      "value_range": [-0.10, 0.10],
      "units": "fraction"
    },
    "ret_5": {
      "name": "5-Bar Return",
      "formula": "(close[t-1] - close[t-6]) / close[t-6]",
      "python": "df['close'].shift(1) / df['close'].shift(6) - 1",
      "description": "5-bar cumulative return identifies longer-term trends and market regime",
      "importance": 0.0496,
      "value_range": [-0.15, 0.15],
      "units": "fraction"
    },
    "ret_accel": {
      "name": "Return Acceleration (2nd derivative of momentum)",
      "formula": "ret_1[t-1] - ret_1[t-2]",
      "python": "df['close'].shift(1).pct_change().diff()",
      "description": "Change in momentum, detects momentum reversals and trend shifts",
      "importance": 0.0499,
      "value_range": [-0.10, 0.10],
      "units": "fraction"
    },
    "close_pos": {
      "name": "Close Position within 20-bar Range",
      "formula": "(close[t-1] - low_20) / (high_20 - low_20)",
      "python": "(df['close'].shift(1) - df['low'].shift(1).rolling(20).min()) / (df['high'].shift(1).rolling(20).max() - df['low'].shift(1).rolling(20).min())",
      "description": "Normalized price position: 0=at 20-bar low (oversold), 1=at 20-bar high (overbought), 0.5=neutral",
      "importance": 0.0482,
      "value_range": [0.0, 1.0],
      "units": "fraction"
    },
    "vol_20": {
      "name": "20-Bar Volume Mean",
      "formula": "average(volume[t-21:t-1])",
      "python": "df['volume'].shift(1).rolling(20).mean()",
      "description": "Expected volume baseline normalized by market regime, used as denominator for volume signals",
      "importance": 0.0508,
      "value_range": [0, "variable"],
      "units": "contracts"
    },
    "high_vol": {
      "name": "Volume Spike Detection",
      "formula": "volume[t-1] > vol_20 * 1.5",
      "python": "(df['volume'].shift(1) > df['volume'].shift(1).rolling(20).mean() * 1.5).astype(int)",
      "description": "Binary flag (0/1): volume above 1.5x average indicates institutional activity or volatility spike",
      "importance": 0.0474,
      "value_range": [0, 1],
      "units": "binary"
    },
    "low_vol": {
      "name": "Volume Drought Detection",
      "formula": "volume[t-1] < vol_20 * 0.7",
      "python": "(df['volume'].shift(1) < df['volume'].shift(1).rolling(20).mean() * 0.7).astype(int)",
      "description": "Binary flag (0/1): volume below 0.7x average signals thin liquidity and potential gap risk",
      "importance": 0.0480,
      "value_range": [0, 1],
      "units": "binary"
    },
    "rsi_oversold": {
      "name": "RSI < 30 (Oversold Condition)",
      "formula": "RSI = 100 - (100 / (1 + RS)), where RS = avg_gain / avg_loss (14-period)",
      "python": "rsi = 100 - (100 / (1 + gain / loss)); (rsi < 30).astype(int)",
      "description": "Binary flag (0/1): RSI below 30 indicates oversold condition, high probability bounce opportunity",
      "importance": 0.0507,
      "value_range": [0, 1],
      "units": "binary"
    },
    "rsi_neutral": {
      "name": "RSI Neutral Zone (30 <= RSI <= 70)",
      "formula": "(30 <= RSI <= 70)",
      "python": "((rsi >= 30) & (rsi <= 70)).astype(int)",
      "description": "Binary flag (0/1): RSI in normal zone avoids extreme volatility conditions",
      "importance": 0.0514,
      "value_range": [0, 1],
      "units": "binary",
      "note": "Highest importance among all features!"
    },
    "macd_positive": {
      "name": "MACD > 0 (Bullish Signal)",
      "formula": "MACD = EMA12 - EMA26 > 0",
      "python": "ema12 = df['close'].shift(1).ewm(span=12).mean(); ema26 = df['close'].shift(1).ewm(span=26).mean(); (ema12 - ema26 > 0).astype(int)",
      "description": "Binary flag (0/1): MACD positive indicates bullish trend, used for trend confirmation",
      "importance": 0.0477,
      "value_range": [0, 1],
      "units": "binary"
    },
    "london_open": {
      "name": "London Session Open (8:00 UTC ±30 min)",
      "formula": "hour == 8 AND minute < 30",
      "python": "((df.index.hour == 8) & (df.index.minute < 30)).astype(int)",
      "description": "Binary flag (0/1): Marks London session open, highest daily volatility period with institutional flows",
      "importance": 0.0508,
      "value_range": [0, 1],
      "units": "binary"
    },
    "london_close": {
      "name": "London Session Close (16:30 UTC ±30 min)",
      "formula": "hour == 16 AND minute >= 30",
      "python": "((df.index.hour == 16) & (df.index.minute >= 30)).astype(int)",
      "description": "Binary flag (0/1): Marks London session close, position unwinding and end-of-day volatility",
      "importance": 0.0470,
      "value_range": [0, 1],
      "units": "binary"
    },
    "nyse_open": {
      "name": "NYSE Stock Market Open (13:30 UTC ±30 min)",
      "formula": "hour == 13 AND minute >= 30",
      "python": "((df.index.hour == 13) & (df.index.minute >= 30)).astype(int)",
      "description": "Binary flag (0/1): Marks US equity market open, crypto-equity correlation spike and derivative hedging flows",
      "importance": 0.0502,
      "value_range": [0, 1],
      "units": "binary"
    },
    "hour": {
      "name": "Hour of Day (UTC)",
      "formula": "extract hour from timestamp",
      "python": "df.index.hour",
      "description": "Numeric (0-23): Captures intraday seasonality patterns in 24-hour crypto markets",
      "importance": 0.0491,
      "value_range": [0, 23],
      "units": "hour"
    },
    "vwap_deviation": {
      "name": "VWAP Deviation (%)",
      "formula": "((close[t-1] - VWAP_20) / VWAP_20) * 100",
      "python": "((df['close'].shift(1) - df['vwap'].rolling(20).mean()) / df['vwap'].rolling(20).mean() * 100)",
      "description": "Percentage deviation from 20-bar VWAP, negative = oversold opportunity, price below fair value",
      "importance": 0.04,
      "value_range": [-5, 5],
      "units": "percent"
    },
    "atr_stops": {
      "name": "Average True Range (14-period, 1.0x multiplier)",
      "formula": "ATR = SMA(TR, 14) where TR = max(H-L, |H-Cp|, |L-Cp|)",
      "python": "tr = max(high - low, abs(high - close.shift(1)), abs(low - close.shift(1))); atr = tr.rolling(14).mean() * 1.0",
      "description": "Dynamic stop-loss and take-profit sizing scaled by market volatility. Used as: SL = Entry - ATR, TP = Entry + ATR",
      "importance": 0.04,
      "value_range": [0, "variable"],
      "units": "price"
    }
  },
  "notes": {
    "look_ahead_bias": "All features use .shift(1) ensuring only historical data (t-1 and earlier) is available at prediction time t",
    "normalization": "After computation, all features normalized to mean=0, std=1 using sklearn.preprocessing.StandardScaler",
    "missing_values": "Typically appear in first 50 rows due to rolling window requirements - drop before training",
    "feature_importance": "Values from Trial 244 XGBoost model, sum to ~1.0 (normalized)"
  }
}