feat: implement SOLUSDT carry-aware trend overlay 15m strategy (closes #216)

templates/solusdt-carry-aware-trend-overlay-15m/README.md

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+# SOLUSDT Carry-aware trend overlay 15m
+
+This strategy implements a carry-aware trend overlay approach for SOLUSDT on 15-minute candles using XGBoost.
+
+## Overview
+
+- **Pair**: SOLUSDT
+- **Timeframe**: 15m
+- **Model**: XGBoost with feature engineering focused on carry-aware trend overlay
+- **Goal**: Combine carry (cost of holding) signals with trend overlay for improved timing
+
+## Features Engineered
+
+1. **Basic returns**: 1, 3, 6, 12, 24 period returns
+2. **Carry-aware features** (proxy for funding rates/cost of carry):
+   - Price momentum (24-period)
+   - Volume momentum (24-period)
+   - Carry proxy (price momentum × volume momentum)
+3. **Trend overlay features**:
+   - Multiple timeframe EMAs (9, 21, 55 periods)
+   - Trend strength and direction between timeframes
+   - Price position relative to each EMA
+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`: 0.006 (60 pips) for ATR-normalized breakout
+- `min_confidence`: 0.50 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/solusdt-carry-aware-trend-overlay-15m/quant.config.json

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+{
+  "pair": "SOLUSDT",
+  "timeframe": "15m",
+  "model_family": "XGBoost",
+  "runtime_target": "edge",
+  "artifact_format": "weights_bundle",
+  "parameters": {
+    "lookback": 100,
+    "horizon": 4,
+    "threshold": 0.006,
+    "min_confidence": 0.5
+  },
+  "training_requirements": [
+    "numpy",
+    "pandas",
+    "scikit-learn",
+    "xgboost",
+    "joblib"
+  ],
+  "inference_requirements": [
+    "numpy",
+    "pandas",
+    "scikit-learn",
+    "xgboost",
+    "joblib"
+  ],
+  "symbol": "SOLUSDT",
+  "description": "SOLUSDT carry-aware trend overlay strategy using XGBoost",
+  "disclaimer": "Educational template only. Not financial advice."
+}

templates/solusdt-carry-aware-trend-overlay-15m/strategy.py

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+import numpy as np
+import pandas as pd
+from xgboost import XGBClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+
+
+SYMBOL = "SOLUSDT"
+MODEL_NAME = "solusdt-carry-aware-trend-overlay-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 _rsi(close, n=14):
+    delta = close.diff()
+    gain = delta.clip(lower=0).ewm(alpha=1 / n, adjust=False).mean()
+    loss = (-delta.clip(upper=0)).ewm(alpha=1 / n, adjust=False).mean()
+    return 100 - 100 / (1 + gain / (loss + 1e-9))
+
+
+def _features(df):
+    c = df["close"]
+    h = df["high"]
+    l = df["low"]
+    o = df["open"]
+    v = df["volume"]
+    rng = (h - l).replace(0, np.nan)
+    f = pd.DataFrame(index=df.index)
+    
+    # Basic returns
+    for n in [1, 3, 6, 12, 24]:
+        f[f"ret{n}"] = c.pct_change(n)
+    
+    # Carry-aware features (proxy for funding rates/cost of carry)
+    # Approximate carry using price momentum and volume
+    f["price_momentum"] = c.pct_change(24)  # 24-period momentum (6 hours for 15m)
+    f["volume_momentum"] = v.pct_change(24)
+    f["carry_proxy"] = f["price_momentum"] * f["volume_momentum"]  # Simple carry proxy
+    
+    # Trend overlay features
+    # Multiple timeframe EMAs for trend detection
+    f["ema_fast"] = c.ewm(span=9, adjust=False).mean()
+    f["ema_medium"] = c.ewm(span=21, adjust=False).mean()
+    f["ema_slow"] = c.ewm(span=55, adjust=False).mean()
+    
+    # Trend strength and direction
+    f["trend_fast_medium"] = (f["ema_fast"] - f["ema_medium"]) / c
+    f["trend_medium_slow"] = (f["ema_medium"] - f["ema_slow"]) / c
+    f["trend_fast_slow"] = (f["ema_fast"] - f["ema_slow"]) / c
+    
+    # Price position relative to EMAs
+    f["price_vs_ema_fast"] = (c - f["ema_fast"]) / c
+    f["price_vs_ema_medium"] = (c - f["ema_medium"]) / c
+    f["price_vs_ema_slow"] = (c - f["ema_slow"]) / c
+    
+    # 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 - o) / rng
+    f["close_pos"] = (c - l) / rng
+    
+    # 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(24).mean()) / (v.rolling(24).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", 0.006))
+    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=200,
+        max_depth=5,
+        learning_rate=0.05,
+        subsample=0.8,
+        colsample_bytree=0.8,
+        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", 0.5))
+    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
+    }}