feat: implement ADAUSDT RSI failure swing filter 30m strategy (closes #219)

ideas/idea-016-adausdt-rsi-failure-swing-filter-30m.md

@@ -41,4 +41,4 @@ ADAUSDT may show repeatable behavior when RSI failure swing filter conditions al
 
 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 #31
+Closes #219

templates/adausdt-rsi-failure-swing-filter-30m/README.md

@@ -0,0 +1,49 @@
+# ADAUSDT RSI failure swing filter 30m
+
+This strategy implements an RSI failure swing filter approach for ADAUSDT on 30-minute candles using ExtraTreesClassifier.
+
+## Overview
+
+- **Pair**: ADAUSDT
+- **Timeframe**: 30m
+- **Model**: ExtraTreesClassifier with feature engineering focused on RSI failure swing patterns
+- **Goal**: Trade RSI failure swings which are strong reversal signals
+
+## Features Engineered
+
+1. **Basic returns**: 1, 3, 6, 12 period returns
+2. **RSI calculation**: Standard 14-period RSI
+3. **RSI failure swing features**:
+   - RSI peak and trough identification
+   - Tracking previous RSI peaks and troughs
+   - Bearish failure swing detection (lower high then break below recent low)
+   - Bullish failure swing detection (higher low then break above recent high)
+   - Combined failure swing signal (bullish - bearish)
+4. **ATR-normalized candle range and close location value** (from idea)
+5. **Distance from EMAs** (from idea): 20, 50, and 200 period EMAs
+6. **Prior swing high and swing low distance** (from idea)
+7. **Volume features**: Z-score and ratio to moving average
+8. **Rolling volatility percentile** (from idea): Fast/slow volatility ratio, volatility percentile
+
+## Configuration
+
+See `quant.config.json` for hyperparameters:
+- `lookback`: 100 candles for prediction
+- `horizon`: 6 candles forward for labeling (3 hours for 30m timeframe)
+- `threshold`: 0.003 (30 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/adausdt-rsi-failure-swing-filter-30m/quant.config.json

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+{
+  "pair": "ADAUSDT",
+  "timeframe": "30m",
+  "model_family": "ExtraTreesClassifier",
+  "runtime_target": "edge",
+  "artifact_format": "weights_bundle",
+  "parameters": {
+    "lookback": 100,
+    "horizon": 6,
+    "threshold": 0.003,
+    "min_confidence": 0.5
+  },
+  "training_requirements": [
+    "numpy",
+    "pandas",
+    "scikit-learn",
+    "joblib"
+  ],
+  "inference_requirements": [
+    "numpy",
+    "pandas",
+    "scikit-learn",
+    "joblib"
+  ],
+  "symbol": "ADAUSDT",
+  "description": "ADAUSDT RSI failure swing filter strategy using ExtraTreesClassifier",
+  "disclaimer": "Educational template only. Not financial advice."
+}

templates/adausdt-rsi-failure-swing-filter-30m/strategy.py

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+import numpy as np
+import pandas as pd
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+
+
+SYMBOL = "ADAUSDT"
+MODEL_NAME = "adausdt-rsi-failure-swing-filter-30m"
+
+
+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)
+    
+    # RSI calculation
+    delta = c.diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+    rs = gain / (loss + 1e-9)
+    rsi = 100 - (100 / (1 + rs))
+    f["rsi"] = rsi
+    
+    # RSI failure swing features
+    # Identify RSI peaks and troughs
+    f["rsi_peak"] = (f["rsi"] > f["rsi"].shift(1)) & (f["rsi"] > f["rsi"].shift(-1))
+    f["rsi_trough"] = (f["rsi"] < f["rsi"].shift(1)) & (f["rsi"] < f["rsi"].shift(-1))
+    
+    # Failure swing detection: 
+    # Bearish failure swing: RSI makes lower high then breaks below recent low
+    # Bullish failure swing: RSI makes higher low then breaks above recent high
+    f["rsi_prev_peak"] = f["rsi"].where(f["rsi_peak"]).ffill()
+    f["rsi_prev_trough"] = f["rsi"].where(f["rsi_trough"]).ffill()
+    
+    # Bearish failure swing signal
+    f["bearish_failure_swing"] = (
+        (f["rsi"] < f["rsi_prev_peak"]) & 
+        (f["rsi"].shift(1) > f["rsi_prev_peak"]) &
+        (f["rsi"] < f["rsi"].rolling(10).min())
+    ).astype(int)
+    
+    # Bullish failure swing signal
+    f["bullish_failure_swing"] = (
+        (f["rsi"] > f["rsi_prev_trough"]) & 
+        (f["rsi"].shift(1) < f["rsi_prev_trough"]) &
+        (f["rsi"] > f["rsi"].rolling(10).max())
+    ).astype(int)
+    
+    # Combined failure swing signal
+    f["failure_swing_signal"] = f["bullish_failure_swing"] - f["bearish_failure_swing"]
+    
+    # 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", 6))  # 6 candles = 3 hours for 30m timeframe
+    threshold = float(params.get("threshold", 0.003))
+    df = _normalise(data)
+    feat = _features(df)
+    y = _labels(df["close"], feat.index, horizon, threshold)
+    
+    model = Pipeline([
+        ("scaler", StandardScaler()),
+        ("clf", ExtraTreesClassifier(
+            n_estimators=200,
+            max_depth=10,
+            min_samples_split=5,
+            min_samples_leaf=2,
+            max_features='sqrt',
+            random_state=42,
+            n_jobs=-1
+        )),
+    ])
+    model.fit(feat.values.astype(np.float32), y.values)
+    preds = model.predict(feat.values.astype(np.float32))
+    
+    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}}
+    
+    proba = model["model"].predict_proba(feat.values.astype(np.float32))[0]
+    klass = int(np.argmax(prob))
+    conf = float(proba[klass])
+    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
+    }}