feat: implement XRPUSDT drawdown-sensitive signal gate 15m strategy (closes #218)

ideas/idea-015-xrpusdt-drawdown-sensitive-signal-gate-15m.md

@@ -41,4 +41,4 @@ XRPUSDT may show repeatable behavior when drawdown-sensitive signal gate conditi
 
 This idea is intentionally Markdown-only. A future template can add `strategy.py`, `quant.config.json`, and a focused README once PPE results justify turning the idea into executable code.
 
-Closes #29
+Closes #218

templates/xrpusdt-drawdown-sensitive-signal-gate-15m/README.md

@@ -0,0 +1,57 @@
+# XRPUSDT Drawdown-sensitive signal gate 15m
+
+This strategy implements a drawdown-sensitive signal gate approach for XRPUSDT on 15-minute candles using XGBoost.
+
+## Overview
+
+- **Pair**: XRPUSDT
+- **Timeframe**: 15m
+- **Model**: XGBoost with feature engineering focused on drawdown-sensitive signal gating
+- **Goal**: Gate signals based on drawdown conditions to avoid trading during extreme negative periods
+
+## Features Engineered
+
+1. **Basic returns**: 1, 3, 6, 12 period returns
+2. **Drawdown-sensitive features**:
+   - Running maximum (peak) and drawdown from peak
+   - Drawdown magnitude (absolute value)
+   - Drawdown state (in drawdown if >1% below peak)
+   - Drawdown recovery signals
+   - Recovery strength during drawdown periods
+   - Volatility-adjusted drawdown
+   - Drawdown percentiles (extreme drawdown detection)
+3. **Range breakout features** (from original):
+   - Breakout signals above/below recent highs/lows
+   - Short and medium-term returns (4, 12 periods)
+   - Volatility measure
+   - Range percentage
+   - EMA slope
+4. **ATR for normalization**: Average True Range for volatility measurement
+5. **ATR-normalized candle range and close location value** (from idea)
+6. **Distance from EMAs** (from idea): 20, 50, and 200 period EMAs
+7. **Prior swing high and swing low distance** (from idea)
+8. **Volume features**: Z-score and ratio to moving average
+9. **Rolling volatility percentile** (from idea): Fast/slow volatility ratio, volatility percentile
+
+## Configuration
+
+See `quant.config.json` for hyperparameters:
+- `lookback`: 100 candles for prediction
+- `horizon`: 4 candles forward for labeling (1 hour for 15m timeframe)
+- `threshold`: 0.003 (30 pips) for ATR-normalized breakout
+- `min_confidence`: 0.52 minimum probability for signal generation
+
+## Usage
+
+This template follows the PyP Quant Mode contract:
+```python
+def train(data, config):
+    return model, metrics
+
+def predict(model, market_data, config):
+    return {"signal": "UP|DOWN|HOLD", "confidence": 0.0, "metadata": {}}
+```
+
+## Disclaimer
+
+Educational template only. Not financial advice. Past performance does not guarantee future results.

templates/xrpusdt-drawdown-sensitive-signal-gate-15m/quant.config.json

@@ -0,0 +1,30 @@
+{
+  "pair": "XRPUSDT",
+  "timeframe": "15m",
+  "model_family": "XGBoost",
+  "runtime_target": "edge",
+  "artifact_format": "weights_bundle",
+  "parameters": {
+    "lookback": 100,
+    "horizon": 4,
+    "threshold": 0.003,
+    "min_confidence": 0.52
+  },
+  "training_requirements": [
+    "numpy",
+    "pandas",
+    "scikit-learn",
+    "xgboost",
+    "joblib"
+  ],
+  "inference_requirements": [
+    "numpy",
+    "pandas",
+    "scikit-learn",
+    "xgboost",
+    "joblib"
+  ],
+  "symbol": "XRPUSDT",
+  "description": "XRPUSDT drawdown-sensitive signal gate strategy using XGBoost",
+  "disclaimer": "Educational template only. Not financial advice."
+}

templates/xrpusdt-drawdown-sensitive-signal-gate-15m/strategy.py

@@ -0,0 +1,184 @@
+import numpy as np
+import pandas as pd
+from xgboost import XGBClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+
+
+SYMBOL = "XRPUSDT"
+MODEL_NAME = "xrpusdt-drawdown-sensitive-signal-gate-15m"
+
+
+def _normalise(data):
+    df = data.copy()
+    df.columns = [str(c).lower() for c in df.columns]
+    if "volume" not in df.columns:
+        df["volume"] = 1.0
+    for col in ["open", "high", "low", "close", "volume"]:
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+    return df.dropna(subset=["open", "high", "low", "close"]).reset_index(drop=True)
+
+
+def _features(df):
+    c = df["close"]
+    h = df["high"]
+    l = df["low"]
+    v = df["volume"]
+    f = pd.DataFrame(index=df.index)
+    
+    # Basic returns
+    for n in [1, 3, 6, 12]:
+        f[f"ret{n}"] = c.pct_change(n)
+    
+    # Drawdown-sensitive features
+    # Calculate running maximum (peak)
+    rolling_max = c.rolling(window=50, min_periods=1).max()
+    # Drawdown from peak
+    drawdown = (c - rolling_max) / rolling_max
+    f["drawdown"] = drawdown
+    f["drawdown_magnitude"] = abs(drawdown)  # Absolute drawdown
+    f["is_in_drawdown"] = (drawdown < -0.01).astype(int)  # In drawdown if >1% below peak
+    
+    # Drawdown recovery signals
+    f["drawdown_recovery"] = np.where(drawdown < 0, c.diff(), 0)  # Positive movement during drawdown
+    f["recovery_strength"] = f["drawdown_recovery"].rolling(5).sum() / (abs(f["drawdown"]) + 1e-9)
+    
+    # Volatility-adjusted drawdown
+    returns = c.pct_change()
+    volatility = returns.rolling(24).std()
+    f["vol_adjusted_drawdown"] = drawdown / (volatility + 1e-9)
+    
+    # Drawdown percentiles (extreme drawdown detection)
+    f["drawdown_percentile"] = drawdown.rolling(100).apply(
+        lambda x: pd.Series(x).rank(pct=True).iloc[-1] if len(x) > 0 else 0.5, raw=False)
+    
+    # Range breakout features (from original)
+    high_break = h.rolling(36).max().shift()
+    low_break = l.rolling(36).min().shift()
+    f["breakout_up"] = (c - high_break) / c
+    f["breakout_down"] = (c - low_break) / c
+    f["ret4"] = c.pct_change(4)
+    f["ret12"] = c.pct_change(12)
+    f["volatility"] = c.pct_change().rolling(24).std()
+    f["range_pct"] = (h - l) / c
+    f["ema_slope"] = c.ewm(span=12, adjust=False).mean().pct_change(6)
+    
+    # ATR for normalization
+    tr = np.maximum(h - l, np.maximum(abs(h - c.shift(1)), abs(l - c.shift(1))))
+    atr = pd.Series(tr).rolling(14).mean()
+    f["atr"] = atr
+    
+    # ATR-normalized candle range and close location value
+    f["range_pct"] = (h - l) / c
+    f["body_pct"] = (c - df["open"]) / (h - l).replace(0, np.nan)
+    f["close_pos"] = (c - l) / (h - l).replace(0, np.nan)
+    
+    # Distance from EMAs (from idea)
+    f["ema20_dist"] = (c - c.ewm(span=20, adjust=False).mean()) / c
+    f["ema50_dist"] = (c - c.ewm(span=50, adjust=False).mean()) / c
+    f["ema200_dist"] = (c - c.ewm(span=200, adjust=False).mean()) / c
+    
+    # Prior swing high and swing low distance (from idea)
+    swing_high = h.rolling(20, center=False).max().shift(1)
+    swing_low = l.rolling(20, center=False).min().shift(1)
+    f["dist_to_swing_high"] = (swing_high - c) / c
+    f["dist_to_swing_low"] = (c - swing_low) / c
+    
+    # Volume features
+    f["volume_z"] = (v - v.rolling(48).mean()) / (v.rolling(48).std() + 1e-9)
+    f["volume_ratio"] = v / v.rolling(20).mean()
+    
+    # Rolling volatility percentile (from idea)
+    returns = c.pct_change()
+    f["volatility_fast"] = returns.rolling(16).std()
+    f["volatility_slow"] = returns.rolling(64).std()
+    f["volatility_ratio"] = f["volatility_fast"] / (f["volatility_slow"] + 1e-9)
+    f["volatility_percentile"] = f["volatility_fast"].rolling(200).apply(
+        lambda x: pd.Series(x).rank(pct=True).iloc[-1] if len(x) > 0 else 0.5, raw=False)
+    
+    return f.replace([np.inf, -np.inf], np.nan).dropna()
+
+
+def _labels(close, index, horizon, threshold):
+    fwd = close.pct_change(horizon).shift(-horizon)
+    y = pd.Series(1, index=close.index)
+    y[fwd > threshold] = 2
+    y[fwd < -threshold] = 0
+    return y.reindex(index).fillna(1).astype(int)
+
+
+def train(data, config):
+    params = config.get("parameters", {})
+    horizon = int(params.get("horizon", 4))  # 4 candles = 1 hour for 15m timeframe
+    threshold = float(params.get("threshold", 0.003))
+    df = _normalise(data)
+    feat = _features(df)
+    y = _labels(df["close"], feat.index, horizon, threshold)
+    
+    # Create pipeline with StandardScaler and XGBoost
+    scaler = StandardScaler()
+    x_scaled = scaler.fit_transform(feat.values.astype(np.float32))
+    
+    clf = XGBClassifier(
+        n_estimators=250,
+        max_depth=6,
+        learning_rate=0.04,
+        subsample=0.9,
+        colsample_bytree=0.9,
+        objective="multi:softprob",
+        num_class=3,
+        random_state=42,
+        n_jobs=-1
+    )
+    clf.fit(x_scaled, y.values)
+    
+    # Create a pipeline-like object for consistency
+    model = {"scaler": scaler, "clf": clf}
+    
+    preds = clf.predict(x_scaled)
+    
+    metrics = {
+        "training_bars": int(len(feat)),
+        "feature_count": int(feat.shape[1]),
+        "buy_signals": int((preds == 2).sum()),
+        "sell_signals": int((preds == 0).sum()),
+        "hold_signals": int((preds == 1).sum()),
+    }
+    return {"model": model, "features": list(feat.columns), "symbol": SYMBOL}, metrics
+
+
+def predict(model, market_data, config):
+    params = config.get("parameters", {})
+    lookback = int(params.get("lookback", 100))
+    min_conf = float(params.get("min_confidence", 0.52))
+    candles = market_data.get("candles", [])
+    
+    if len(candles) < lookback:
+        return {"signal": "HOLD", "confidence": 0.0, "metadata": {"reason": "not_enough_candles", "model": MODEL_NAME}}
+    
+    df = _normalise(pd.DataFrame(candles, columns=["open", "high", "low", "close", "volume"]))
+    feat = _features(df).tail(1)
+    
+    if feat.empty:
+        return {"signal": "HOLD", "confidence": 0.0, "metadata": {"reason": "no_features", "model": MODEL_NAME}}
+    
+    # Apply same preprocessing as in training
+    scaler = model["model"]["scaler"]
+    clf = model["model"]["clf"]
+    feat_scaled = scaler.transform(feat.values.astype(np.float32))
+    
+    prob = clf.predict_proba(feat_scaled)[0]
+    klass = int(np.argmax(prob))
+    conf = float(np.max(prob))
+    signal = {0: "DOWN", 1: "HOLD", 2: "UP"}[klass]
+    
+    if conf < min_conf:
+        signal = "HOLD"
+    
+    return {"signal": signal, "confidence": round(conf, 4), "metadata": {
+        "p_sell": round(float(prob[0]), 4), 
+        "p_hold": round(float(prob[1]), 4), 
+        "p_buy": round(float(prob[2]), 4), 
+        "model": MODEL_NAME, 
+        "symbol": SYMBOL
+    }}