Backtest tuning, data freshness guard, Telegram improvements

- Drop USD/JPY from scan pairs (zero signals across all timeframes)
- Disable divergence indicator (zero impact in 14-config lab test)
- Parallelize evaluate.py with ProcessPoolExecutor (16 cores)
- Add --pair CLI filter to evaluate.py for single-pair backtests
- Add data freshness guard: stale cache auto-invalidated, primary TF
  stale data suppresses signals to prevent trading on old data
- Add /cache command: shows fetch age and last bar per pair/TF
- Add /clearcache command: wipes in-memory cache for fresh API fetch
- Remove BB from WebSocket breach alerts (keep structural levels only)
- Show live prices only for WS-monitored pairs (EUR/USD, XAU/USD)
- Replace arrow emojis with text tags in indicator lines

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backtest/evaluate.py

@@ -0,0 +1,613 @@
+#!/usr/bin/env python3
+"""
+ForexBot2 — Historical Signal Evaluation
+=========================================
+Walks the engine over the last 7 days of market data for all scan pairs,
+records every signal and trade setup, then checks outcomes on M5 data.
+
+GPU-accelerated outcome evaluation via CuPy (RTX 4070 SUPER).
+Falls back to NumPy if CuPy/CUDA is unavailable.
+
+Usage:
+    python backtest/evaluate.py                  # full 7-day eval, M15+H1
+    python backtest/evaluate.py --tf H1          # single timeframe
+    python backtest/evaluate.py --days 3         # shorter window
+    python backtest/evaluate.py --no-cache       # re-download all data
+"""
+
+import argparse
+import csv
+import os
+import sys
+import time
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from dataclasses import dataclass, field
+from datetime import datetime, timezone, timedelta
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from config import (
+    PlatformConfig, Timeframe, SignalStrength,
+    EURUSD, USDJPY, GBPUSD, XAUUSD, PAIR_INFO,
+)
+from data_provider import ForexDataProvider
+from signal_aggregator import SignalAggregator
+
+# ── GPU setup ────────────────────────────────────────────────────────
+try:
+    if "CUDA_PATH" not in os.environ:
+        _nvidia_dir = os.path.join(
+            sys.prefix, "lib", "python3.12", "site-packages", "nvidia"
+        )
+        if os.path.isdir(_nvidia_dir):
+            os.environ["CUDA_PATH"] = _nvidia_dir
+    import cupy as cp
+    _test = cp.array([1.0, 2.0])
+    _ = cp.where(_test > 0.5)
+    GPU = True
+    GPU_NAME = cp.cuda.runtime.getDeviceProperties(0)["name"].decode()
+    print(f"GPU: {GPU_NAME} (CuPy {cp.__version__})")
+    del _test
+except Exception:
+    import numpy as cp  # type: ignore[no-redef]
+    GPU = False
+    print("GPU: not available — falling back to NumPy")
+
+# ── Constants ────────────────────────────────────────────────────────
+BACKTEST_DIR = Path(__file__).resolve().parent
+DATA_DIR = BACKTEST_DIR / "data"
+RESULTS_DIR = BACKTEST_DIR / "results"
+
+SCAN_PAIRS = [EURUSD, GBPUSD, XAUUSD]
+
+EVAL_TIMEFRAMES = [Timeframe.M15, Timeframe.H1]
+
+TF_CONFIRMATIONS = {
+    Timeframe.M5:  [Timeframe.M15, Timeframe.H1],
+    Timeframe.M15: [Timeframe.M5, Timeframe.H1],
+    Timeframe.H1:  [Timeframe.M15, Timeframe.H4],
+    Timeframe.H4:  [Timeframe.H1, Timeframe.D1],
+}
+
+ALL_TFS_NEEDED = {
+    Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4, Timeframe.D1,
+}
+
+OUTCOME_TF = Timeframe.M5
+
+MIN_LOOKBACK_BARS = 250
+
+
+@dataclass
+class Trade:
+    pair: str
+    display: str
+    timeframe: str
+    entry_time: pd.Timestamp
+    direction: str
+    entry_price: float
+    stop_loss: float
+    take_profit_1: float
+    take_profit_2: Optional[float]
+    risk_reward: float
+    risk_pips: float
+    reward_pips: float
+    signal_quality: str
+    confidence: float
+    score: float
+    regime: str
+    outcome: str = "OPEN"
+    exit_price: float = 0.0
+    exit_time: Optional[pd.Timestamp] = None
+    pnl_pips: float = 0.0
+    bars_held: int = 0
+
+
+# ══════════════════════════════════════════════════════════════════════
+#  DATA FETCHING & CACHING
+# ══════════════════════════════════════════════════════════════════════
+
+def _cache_path(symbol: str, tf: Timeframe) -> Path:
+    safe = symbol.replace("/", "_").replace("=", "")
+    return DATA_DIR / f"{safe}_{tf.value}.parquet"
+
+
+def fetch_all_data(
+    pairs: List[str],
+    timeframes,
+    lookback_days: int = 67,
+    use_cache: bool = True,
+) -> Dict[str, Dict[Timeframe, pd.DataFrame]]:
+    """Fetch OHLCV for all pair×TF combos, cache to parquet."""
+    config = PlatformConfig()
+    config.data_lookback_days = lookback_days
+    dp = ForexDataProvider(config)
+    data: Dict[str, Dict[Timeframe, pd.DataFrame]] = {}
+
+    total = len(pairs) * len(timeframes)
+    done = 0
+
+    for symbol in pairs:
+        data[symbol] = {}
+        display = PAIR_INFO.get(symbol, {}).get("display", symbol)
+        for tf in timeframes:
+            done += 1
+            cache = _cache_path(symbol, tf)
+            if use_cache and cache.exists():
+                mtime = datetime.fromtimestamp(cache.stat().st_mtime, tz=timezone.utc)
+                age_hours = (datetime.now(timezone.utc) - mtime).total_seconds() / 3600
+                if age_hours < 4:
+                    df = pd.read_parquet(cache)
+                    data[symbol][tf] = df
+                    print(f"  [{done}/{total}] {display} {tf.value} — cached ({len(df)} bars)")
+                    continue
+
+            print(f"  [{done}/{total}] {display} {tf.value} — fetching...", end="", flush=True)
+            try:
+                df = dp.fetch_ohlcv(symbol, tf, lookback_days=lookback_days)
+                data[symbol][tf] = df
+                try:
+                    df.to_parquet(cache)
+                except Exception:
+                    pass
+                print(f" {len(df)} bars")
+            except Exception as e:
+                print(f" FAILED: {e}")
+
+    return data
+
+
+# ══════════════════════════════════════════════════════════════════════
+#  WALK-FORWARD EVALUATION
+# ══════════════════════════════════════════════════════════════════════
+
+def walk_forward(
+    pair: str,
+    eval_tf: Timeframe,
+    all_data: Dict[Timeframe, pd.DataFrame],
+    eval_days: int,
+) -> List[Trade]:
+    """Walk through eval_days of candles, run the engine at each bar."""
+    primary_df = all_data.get(eval_tf)
+    if primary_df is None or primary_df.empty:
+        return []
+
+    conf_tfs = TF_CONFIRMATIONS.get(eval_tf, [Timeframe.M15, Timeframe.H4])
+    display = PAIR_INFO.get(pair, {}).get("display", pair)
+
+    cutoff = primary_df.index[-1] - pd.Timedelta(days=eval_days)
+    eval_indices = primary_df.index[primary_df.index >= cutoff]
+
+    if len(eval_indices) == 0:
+        return []
+
+    first_eval_pos = primary_df.index.get_loc(eval_indices[0])
+    if first_eval_pos < MIN_LOOKBACK_BARS:
+        first_eval_pos = MIN_LOOKBACK_BARS
+        eval_indices = primary_df.index[first_eval_pos:]
+
+    config = PlatformConfig()
+    config.primary_timeframe = eval_tf
+    config.confirmation_timeframes = conf_tfs
+    aggregator = SignalAggregator(config)
+
+    cooldown_bars = {
+        Timeframe.M5: 24, Timeframe.M15: 10,
+        Timeframe.H1: 5, Timeframe.H4: 3,
+    }.get(eval_tf, 6)
+
+    trades: List[Trade] = []
+    total = len(eval_indices)
+    bars_until_next = 0
+
+    for i, eval_time in enumerate(eval_indices):
+        if (i + 1) % max(1, total // 10) == 0:
+            print(f"    {display} {eval_tf.value}: {i+1}/{total} candles...", flush=True)
+
+        if bars_until_next > 0:
+            bars_until_next -= 1
+            continue
+
+        dfs: Dict[Timeframe, pd.DataFrame] = {}
+        dfs[eval_tf] = primary_df.loc[:eval_time]
+
+        for ctf in conf_tfs:
+            ctf_df = all_data.get(ctf)
+            if ctf_df is not None and not ctf_df.empty:
+                dfs[ctf] = ctf_df.loc[:eval_time]
+
+        try:
+            report = aggregator.analyze_from_dataframes(
+                symbol=pair, dfs=dfs, use_news=False,
+            )
+        except Exception:
+            continue
+
+        if report.trade_setup and report.signal_quality in ("A+", "A", "B", "C"):
+            ts = report.trade_setup
+            trades.append(Trade(
+                pair=pair,
+                display=display,
+                timeframe=eval_tf.value,
+                entry_time=eval_time,
+                direction=ts.direction,
+                entry_price=ts.entry_price,
+                stop_loss=ts.stop_loss,
+                take_profit_1=ts.take_profit_1,
+                take_profit_2=ts.take_profit_2,
+                risk_reward=ts.risk_reward,
+                risk_pips=ts.risk_pips,
+                reward_pips=ts.reward_pips,
+                signal_quality=report.signal_quality,
+                confidence=report.final_confidence,
+                score=report.final_score,
+                regime=report.market_regime,
+            ))
+            bars_until_next = cooldown_bars
+
+    return trades
+
+
+def _walk_forward_worker(args):
+    """Top-level worker for multiprocessing (must be picklable)."""
+    pair, tf, pair_data, eval_days = args
+    display = PAIR_INFO.get(pair, {}).get("display", pair)
+    trades = walk_forward(pair, tf, pair_data, eval_days)
+    return pair, tf, display, trades
+
+
+# ══════════════════════════════════════════════════════════════════════
+#  GPU-ACCELERATED OUTCOME EVALUATION
+# ══════════════════════════════════════════════════════════════════════
+
+def evaluate_outcomes(
+    trades: List[Trade],
+    m5_data: Dict[str, pd.DataFrame],
+    max_bars: int = 500,
+) -> List[Trade]:
+    """
+    For each trade, scan forward in M5 data to check if TP1 or SL hit first.
+    Uses GPU (CuPy) for vectorized parallel evaluation across all trades per pair.
+    """
+    if not trades:
+        return trades
+
+    pairs_in_trades = set(t.pair for t in trades)
+    label = "GPU" if GPU else "CPU"
+    print(f"\nEvaluating {len(trades)} trade outcomes on {label}...")
+
+    for pair in pairs_in_trades:
+        df = m5_data.get(pair)
+        if df is None or df.empty:
+            continue
+
+        pair_trades = [t for t in trades if t.pair == pair]
+        if not pair_trades:
+            continue
+
+        highs_np = df["High"].values.astype(np.float64)
+        lows_np = df["Low"].values.astype(np.float64)
+        closes_np = df["Close"].values.astype(np.float64)
+        timestamps = df.index
+
+        highs_gpu = cp.asarray(highs_np)
+        lows_gpu = cp.asarray(lows_np)
+
+        for trade in pair_trades:
+            start_mask = timestamps > trade.entry_time
+            start_indices = np.where(start_mask)[0]
+            if len(start_indices) == 0:
+                continue
+
+            start = int(start_indices[0])
+            end = min(start + max_bars, len(highs_gpu))
+
+            if start >= end:
+                continue
+
+            h_slice = highs_gpu[start:end]
+            l_slice = lows_gpu[start:end]
+
+            if trade.direction == "BUY":
+                tp_mask = h_slice >= trade.take_profit_1
+                sl_mask = l_slice <= trade.stop_loss
+            else:
+                tp_mask = l_slice <= trade.take_profit_1
+                sl_mask = h_slice >= trade.stop_loss
+
+            tp_hits = cp.where(tp_mask)[0]
+            sl_hits = cp.where(sl_mask)[0]
+
+            tp_idx = int(tp_hits[0]) if len(tp_hits) > 0 else max_bars + 1
+            sl_idx = int(sl_hits[0]) if len(sl_hits) > 0 else max_bars + 1
+
+            pip_val = PAIR_INFO.get(pair, {}).get("pip_value", 0.0001)
+
+            if tp_idx <= sl_idx and tp_idx < max_bars + 1:
+                trade.outcome = "WIN"
+                trade.bars_held = tp_idx + 1
+                real_idx = start + tp_idx
+                trade.exit_price = trade.take_profit_1
+                trade.exit_time = timestamps[real_idx]
+                trade.pnl_pips = trade.reward_pips
+            elif sl_idx < tp_idx and sl_idx < max_bars + 1:
+                trade.outcome = "LOSS"
+                trade.bars_held = sl_idx + 1
+                real_idx = start + sl_idx
+                trade.exit_price = trade.stop_loss
+                trade.exit_time = timestamps[real_idx]
+                trade.pnl_pips = -trade.risk_pips
+            else:
+                trade.outcome = "OPEN"
+                last_idx = end - 1
+                trade.exit_time = timestamps[last_idx]
+                exit_p = float(closes_np[last_idx])
+                trade.exit_price = exit_p
+                if trade.direction == "BUY":
+                    trade.pnl_pips = (exit_p - trade.entry_price) / pip_val
+                else:
+                    trade.pnl_pips = (trade.entry_price - exit_p) / pip_val
+                trade.bars_held = end - start
+
+    return trades
+
+
+# ══════════════════════════════════════════════════════════════════════
+#  REPORTING
+# ══════════════════════════════════════════════════════════════════════
+
+def print_report(trades: List[Trade], eval_days: int):
+    print("\n")
+    print("=" * 90)
+    print(f"  FOREXBOT2 — ENGINE EVALUATION REPORT ({eval_days}-DAY)")
+    print(f"  {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}")
+    print("=" * 90)
+
+    if not trades:
+        print("\n  No trades generated.\n")
+        return
+
+    closed = [t for t in trades if t.outcome in ("WIN", "LOSS")]
+    wins = [t for t in closed if t.outcome == "WIN"]
+    losses = [t for t in closed if t.outcome == "LOSS"]
+    still_open = [t for t in trades if t.outcome == "OPEN"]
+
+    # ── Overall ──────────────────────────────────────────────────
+    print(f"\n  OVERALL")
+    print(f"  {'─' * 50}")
+    print(f"  Total signals with trade setup: {len(trades)}")
+    print(f"  Closed: {len(closed)}  |  Still open: {len(still_open)}")
+
+    if closed:
+        win_rate = len(wins) / len(closed) * 100
+        total_pnl = sum(t.pnl_pips for t in closed)
+        avg_win = np.mean([t.pnl_pips for t in wins]) if wins else 0
+        avg_loss = np.mean([abs(t.pnl_pips) for t in losses]) if losses else 0
+        gross_profit = sum(t.pnl_pips for t in wins) if wins else 0
+        gross_loss = sum(abs(t.pnl_pips) for t in losses) if losses else 0
+        profit_factor = gross_profit / gross_loss if gross_loss > 0 else float("inf")
+        avg_rr = np.mean([t.risk_reward for t in closed])
+        avg_bars = np.mean([t.bars_held for t in closed])
+
+        print(f"  Win rate:       {win_rate:.1f}%  ({len(wins)}W / {len(losses)}L)")
+        print(f"  Profit factor:  {profit_factor:.2f}")
+        print(f"  Total P&L:      {total_pnl:+.1f} pips")
+        print(f"  Avg win:        +{avg_win:.1f} pips  |  Avg loss: -{avg_loss:.1f} pips")
+        print(f"  Avg R:R setup:  1:{avg_rr:.1f}")
+        print(f"  Avg hold:       {avg_bars:.0f} M5 bars ({avg_bars * 5:.0f} min)")
+
+        # Consecutive streaks
+        max_wins = max_losses = cur_wins = cur_losses = 0
+        for t in closed:
+            if t.outcome == "WIN":
+                cur_wins += 1
+                cur_losses = 0
+                max_wins = max(max_wins, cur_wins)
+            else:
+                cur_losses += 1
+                cur_wins = 0
+                max_losses = max(max_losses, cur_losses)
+        print(f"  Max streak:     {max_wins}W / {max_losses}L")
+
+    # ── By pair ──────────────────────────────────────────────────
+    print(f"\n  BY PAIR")
+    print(f"  {'─' * 50}")
+    print(f"  {'Pair':<12} {'Trades':>7} {'Closed':>7} {'Win%':>6} {'P&L':>10} {'PF':>7}")
+
+    for pair in SCAN_PAIRS:
+        display = PAIR_INFO.get(pair, {}).get("display", pair)
+        pt = [t for t in trades if t.pair == pair]
+        pc = [t for t in pt if t.outcome in ("WIN", "LOSS")]
+        pw = [t for t in pc if t.outcome == "WIN"]
+        if not pt:
+            continue
+        wr = (len(pw) / len(pc) * 100) if pc else 0
+        pnl = sum(t.pnl_pips for t in pc)
+        gp = sum(t.pnl_pips for t in pw) if pw else 0
+        gl = sum(abs(t.pnl_pips) for t in pc if t.outcome == "LOSS") or 1
+        pf = gp / gl if gl > 0 else 0
+        print(f"  {display:<12} {len(pt):>7} {len(pc):>7} {wr:>5.1f}% {pnl:>+9.1f}p {pf:>6.2f}")
+
+    # ── By timeframe ─────────────────────────────────────────────
+    print(f"\n  BY TIMEFRAME")
+    print(f"  {'─' * 50}")
+    print(f"  {'TF':<8} {'Trades':>7} {'Closed':>7} {'Win%':>6} {'P&L':>10} {'PF':>7}")
+
+    tfs = sorted(set(t.timeframe for t in trades))
+    for tf in tfs:
+        tt = [t for t in trades if t.timeframe == tf]
+        tc = [t for t in tt if t.outcome in ("WIN", "LOSS")]
+        tw = [t for t in tc if t.outcome == "WIN"]
+        if not tt:
+            continue
+        wr = (len(tw) / len(tc) * 100) if tc else 0
+        pnl = sum(t.pnl_pips for t in tc)
+        gp = sum(t.pnl_pips for t in tw) if tw else 0
+        gl = sum(abs(t.pnl_pips) for t in tc if t.outcome == "LOSS") or 1
+        pf = gp / gl if gl > 0 else 0
+        print(f"  {tf:<8} {len(tt):>7} {len(tc):>7} {wr:>5.1f}% {pnl:>+9.1f}p {pf:>6.2f}")
+
+    # ── By quality grade ─────────────────────────────────────────
+    print(f"\n  BY QUALITY GRADE")
+    print(f"  {'─' * 50}")
+    print(f"  {'Grade':<8} {'Trades':>7} {'Closed':>7} {'Win%':>6} {'P&L':>10} {'Avg RR':>7}")
+
+    for grade in ("A+", "A", "B", "C"):
+        gt = [t for t in trades if t.signal_quality == grade]
+        gc = [t for t in gt if t.outcome in ("WIN", "LOSS")]
+        gw = [t for t in gc if t.outcome == "WIN"]
+        if not gt:
+            continue
+        wr = (len(gw) / len(gc) * 100) if gc else 0
+        pnl = sum(t.pnl_pips for t in gc)
+        arr = np.mean([t.risk_reward for t in gt])
+        print(f"  {grade:<8} {len(gt):>7} {len(gc):>7} {wr:>5.1f}% {pnl:>+9.1f}p {arr:>6.1f}")
+
+    # ── By regime ────────────────────────────────────────────────
+    print(f"\n  BY MARKET REGIME")
+    print(f"  {'─' * 50}")
+    print(f"  {'Regime':<16} {'Trades':>7} {'Win%':>6} {'P&L':>10}")
+
+    for regime in sorted(set(t.regime for t in trades)):
+        rt = [t for t in trades if t.regime == regime]
+        rc = [t for t in rt if t.outcome in ("WIN", "LOSS")]
+        rw = [t for t in rc if t.outcome == "WIN"]
+        wr = (len(rw) / len(rc) * 100) if rc else 0
+        pnl = sum(t.pnl_pips for t in rc)
+        label = regime.replace("_", " ").title()
+        print(f"  {label:<16} {len(rt):>7} {wr:>5.1f}% {pnl:>+9.1f}p")
+
+    # ── Direction ────────────────────────────────────────────────
+    print(f"\n  BY DIRECTION")
+    print(f"  {'─' * 50}")
+    for d in ("BUY", "SELL"):
+        dt = [t for t in trades if t.direction == d]
+        dc = [t for t in dt if t.outcome in ("WIN", "LOSS")]
+        dw = [t for t in dc if t.outcome == "WIN"]
+        wr = (len(dw) / len(dc) * 100) if dc else 0
+        pnl = sum(t.pnl_pips for t in dc)
+        print(f"  {d:<8} {len(dt):>7} trades  {wr:>5.1f}% win  {pnl:>+9.1f}p")
+
+    print(f"\n{'=' * 90}\n")
+
+
+def save_trades_csv(trades: List[Trade], filename: str):
+    path = RESULTS_DIR / filename
+    with open(path, "w", newline="") as f:
+        writer = csv.writer(f)
+        writer.writerow([
+            "pair", "display", "timeframe", "entry_time", "direction",
+            "entry_price", "stop_loss", "take_profit_1", "risk_reward",
+            "risk_pips", "reward_pips", "signal_quality", "confidence",
+            "score", "regime", "outcome", "exit_price", "exit_time",
+            "pnl_pips", "bars_held",
+        ])
+        for t in trades:
+            writer.writerow([
+                t.pair, t.display, t.timeframe, t.entry_time, t.direction,
+                f"{t.entry_price:.5f}", f"{t.stop_loss:.5f}", f"{t.take_profit_1:.5f}",
+                f"{t.risk_reward:.2f}", f"{t.risk_pips:.1f}", f"{t.reward_pips:.1f}",
+                t.signal_quality, f"{t.confidence:.1f}", f"{t.score:.1f}",
+                t.regime, t.outcome, f"{t.exit_price:.5f}",
+                t.exit_time or "", f"{t.pnl_pips:.1f}", t.bars_held,
+            ])
+    print(f"Trades saved to {path}")
+
+
+# ══════════════════════════════════════════════════════════════════════
+#  MAIN
+# ══════════════════════════════════════════════════════════════════════
+
+def main():
+    parser = argparse.ArgumentParser(description="ForexBot2 signal engine evaluator")
+    parser.add_argument("--days", type=int, default=7, help="Evaluation window in days (default: 7)")
+    parser.add_argument("--tf", default=None, help="Single timeframe to evaluate (M15, H1)")
+    parser.add_argument("--no-cache", action="store_true", help="Re-download all data")
+    parser.add_argument("--max-outcome-bars", type=int, default=1000,
+                        help="Max M5 bars to scan for SL/TP hit (default: 1000 = ~83h)")
+    parser.add_argument("--pair", default=None,
+                        help="Single pair to evaluate (EURUSD, USDJPY, GBPUSD, XAUUSD)")
+    args = parser.parse_args()
+
+    eval_tfs = EVAL_TIMEFRAMES
+    if args.tf:
+        tf_map = {"M5": Timeframe.M5, "M15": Timeframe.M15, "H1": Timeframe.H1, "H4": Timeframe.H4}
+        if args.tf.upper() not in tf_map:
+            sys.exit(f"Invalid timeframe: {args.tf}. Choose from: {list(tf_map.keys())}")
+        eval_tfs = [tf_map[args.tf.upper()]]
+
+    pair_map = {"EURUSD": EURUSD, "USDJPY": USDJPY, "GBPUSD": GBPUSD, "XAUUSD": XAUUSD}
+    scan_pairs = SCAN_PAIRS
+    if args.pair:
+        key = args.pair.upper().replace("/", "")
+        if key not in pair_map:
+            sys.exit(f"Invalid pair: {args.pair}. Choose from: {list(pair_map.keys())}")
+        scan_pairs = [pair_map[key]]
+
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+    RESULTS_DIR.mkdir(parents=True, exist_ok=True)
+
+    # ── 1. Fetch data ────────────────────────────────────────────
+    lookback = args.days + 60
+    print(f"\n{'=' * 60}")
+    print(f"  Fetching {lookback} days of data for {len(scan_pairs)} pair(s)...")
+    print(f"{'=' * 60}")
+    t0 = time.time()
+    all_data = fetch_all_data(
+        scan_pairs, ALL_TFS_NEEDED,
+        lookback_days=lookback,
+        use_cache=not args.no_cache,
+    )
+    print(f"  Data fetched in {time.time() - t0:.1f}s")
+
+    # ── 2. Walk-forward evaluation (multi-core) ──────────────────
+    print(f"\n{'=' * 60}")
+    n_workers = min(os.cpu_count() or 4, 16)
+    print(f"  Walk-forward evaluation: {args.days} days, TFs: {[tf.value for tf in eval_tfs]}")
+    print(f"  Using {n_workers} CPU cores")
+    print(f"{'=' * 60}")
+    t1 = time.time()
+
+    jobs = []
+    for pair in scan_pairs:
+        pair_data = all_data.get(pair, {})
+        if not pair_data:
+            display = PAIR_INFO.get(pair, {}).get("display", pair)
+            print(f"  {display}: no data, skipping")
+            continue
+        for tf in eval_tfs:
+            jobs.append((pair, tf, pair_data, args.days))
+
+    all_trades: List[Trade] = []
+    with ProcessPoolExecutor(max_workers=n_workers) as pool:
+        futures = {pool.submit(_walk_forward_worker, job): job for job in jobs}
+        for future in as_completed(futures):
+            pair, tf, display, trades = future.result()
+            print(f"  {display} {tf.value}: {len(trades)} trade setups")
+            all_trades.extend(trades)
+
+    print(f"\n  Walk-forward complete in {time.time() - t1:.1f}s")
+    print(f"  Total trade setups: {len(all_trades)}")
+
+    # ── 3. Outcome evaluation (GPU) ──────────────────────────────
+    m5_data = {pair: all_data.get(pair, {}).get(Timeframe.M5) for pair in scan_pairs}
+    m5_data = {k: v for k, v in m5_data.items() if v is not None}
+
+    t2 = time.time()
+    all_trades = evaluate_outcomes(all_trades, m5_data, max_bars=args.max_outcome_bars)
+    print(f"  Outcomes evaluated in {time.time() - t2:.3f}s")
+
+    # ── 4. Report ────────────────────────────────────────────────
+    print_report(all_trades, args.days)
+
+    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+    save_trades_csv(all_trades, f"eval_{args.days}d_{ts}.csv")
+
+
+if __name__ == "__main__":
+    main()

config.py

@@ -76,25 +76,20 @@ class TechnicalConfig:
 
     atr_period: int = 14
 
-    stoch_k: int = 14
-    stoch_d: int = 3
-    stoch_overbought: float = 80.0
-    stoch_oversold: float = 20.0
-
     adx_period: int = 14
     adx_strong_trend: float = 25.0
     adx_very_strong: float = 50.0
 
-    ichimoku_tenkan: int = 9
-    ichimoku_kijun: int = 26
-    ichimoku_senkou_b: int = 52
-
-    volume_ma_period: int = 20
-    pivot_lookback: int = 1
     divergence_lookback: int = 14
     swing_sr_lookback: int = 50
 
-    disabled_indicators: List[str] = field(default_factory=list)
+    disabled_indicators: List[str] = field(default_factory=lambda: ["divergence"])
+
+    ema_mode: str = "4ema"          # "4ema" (9/21/50/200) or "2ema" (50/200)
+    rsi_mode: str = "classic"       # "classic" or "regime_aware"
+    macd_as_pillar: bool = False    # promote MACD histogram to scored signal
+
+    regime_weights_override: Dict[str, Dict[str, float]] = field(default_factory=dict)
 
 
 @dataclass
@@ -106,8 +101,8 @@ class SignalConfig:
         "ema_trend": 1.0,
         "rsi": 1.5,
         "bollinger": 1.5,
-        "candlestick": 1.5,
+        "candlestick": 1.0,
-        "divergence": 1.0,
+        "divergence": 1.5,
     })
 
     mtf_agreement_threshold: int = 2  # of 3 timeframes
@@ -140,9 +135,13 @@ class FilterConfig:
     regime_filter_timeframe: Timeframe = Timeframe.H4
     regime_filter_min_adx: float = 25.0
 
-    session_filter: bool = False
+    session_filter: bool = True
     session_start_hour: int = 7
     session_end_hour: int = 16
+    blocked_hours_utc: List[int] = field(default_factory=lambda: [0, 1, 2, 5, 6, 8, 12, 13, 14, 15, 16, 17, 21, 22, 23])
+
+    sell_filter: bool = True
+    sell_allowed_pairs: List[str] = field(default_factory=lambda: ["XAU/USD"])
 
 
 @dataclass
@@ -169,7 +168,7 @@ class PlatformConfig:
     refresh_interval: int = 300
 
     scan_pairs: List[str] = field(
-        default_factory=lambda: [EURUSD, USDJPY, GBPUSD, XAUUSD]
+        default_factory=lambda: [EURUSD, GBPUSD, XAUUSD]
     )
     scan_timeframes: List[Timeframe] = field(
         default_factory=lambda: [Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4]

data_provider.py

@@ -95,7 +95,11 @@ class ForexDataProvider:
         ):
             cached = self._cache.get(cache_key)
             if cached is not None and not cached.empty:
-                return cached
+                if self._is_data_fresh(cached, symbol, timeframe):
+                    return cached
+                logger.warning(f"{symbol} {timeframe.value}: cached data is stale, refetching")
+                del self._cache[cache_key]
+                del self._cache_timestamps[cache_key]
 
         outputsize = max(
             300,
@@ -104,10 +108,48 @@ class ForexDataProvider:
         df = self._fetch_twelve_data(symbol, timeframe, outputsize)
         df = self._validate_and_clean(df, symbol, timeframe)
 
+        if not self._is_data_fresh(df, symbol, timeframe):
+            logger.warning(
+                f"STALE DATA from API: {symbol} {timeframe.value} — "
+                f"last bar {df.index[-1]} is too old. Signal quality may be degraded."
+            )
+
         self._cache[cache_key] = df
         self._cache_timestamps[cache_key] = now
         return df
 
+    def _is_data_fresh(self, df: pd.DataFrame, symbol: str, timeframe: Timeframe) -> bool:
+        """Check if the last candle is within acceptable age for this timeframe."""
+        if df is None or df.empty:
+            return False
+
+        now = pd.Timestamp.now(tz="UTC")
+
+        if now.weekday() >= 5:
+            return True
+
+        last_bar = df.index[-1]
+        candle_seconds = self.CANDLE_SECONDS.get(timeframe, 3600)
+        max_age_seconds = candle_seconds * 3
+        age_seconds = (now - last_bar).total_seconds()
+
+        if age_seconds > max_age_seconds:
+            logger.warning(
+                f"{symbol} {timeframe.value}: last bar {last_bar.strftime('%Y-%m-%d %H:%M')} "
+                f"is {age_seconds / 60:.0f}m old (limit: {max_age_seconds / 60:.0f}m)"
+            )
+            return False
+
+        return True
+
+    def clear_cache(self) -> int:
+        """Wipe all cached data. Returns number of entries cleared."""
+        count = len(self._cache)
+        self._cache.clear()
+        self._cache_timestamps.clear()
+        logger.info(f"Cache cleared: {count} entries wiped")
+        return count
+
     def get_latest_price(self, symbol: str = USDJPY) -> Optional[float]:
         try:
             df = self.fetch_ohlcv(symbol, Timeframe.M5, lookback_days=1)
@@ -255,7 +297,6 @@ class ForexDataProvider:
 
         min_required = max(
             self.config.technical.ema_trend,
-            self.config.technical.ichimoku_senkou_b,
             200,
         ) + 10
 

engine.py

@@ -91,7 +91,7 @@ class TechnicalEngine:
         if "adx"         not in disabled: self._compute_adx(df, result)
         if "atr"         not in disabled: self._compute_atr(df, result)
 
-        self._compute_macd_internal(df, result)
+        self._compute_macd(df, result)
 
         if "divergence"  not in disabled: self._detect_divergences(df, result)
         if "structure"   not in disabled: self._compute_structure_sr(df, result)
@@ -100,6 +100,10 @@ class TechnicalEngine:
         # ── Classify market regime ─────────────────────────────────
         self._classify_regime(df, result)
 
+        # ── Regime-aware RSI reinterpretation ──────────────────────
+        if self.config.rsi_mode == "regime_aware" and "rsi" not in disabled:
+            self._reinterpret_rsi_for_regime(result)
+
         # ── Compute composite signal ───────────────────────────────
         self._compute_composite(result)
 
@@ -110,7 +114,87 @@ class TechnicalEngine:
     # ══════════════════════════════════════════════════════════════
 
     def _compute_ema_signals(self, df: pd.DataFrame, result: TechnicalAnalysis):
-        """EMA trend alignment analysis."""
+        """EMA trend alignment — supports 4-EMA and 2-EMA modes."""
+        if self.config.ema_mode == "2ema":
+            self._compute_ema_2(df, result)
+        else:
+            self._compute_ema_4(df, result)
+
+    def _compute_ema_2(self, df: pd.DataFrame, result: TechnicalAnalysis):
+        """EMA 50/200 trend alignment — cleaner, less noise on H1."""
+        c = self.config
+        close = df["Close"]
+
+        ema50 = close.ewm(span=c.ema_slow, adjust=False).mean()
+        ema200 = close.ewm(span=c.ema_trend, adjust=False).mean() if len(close) >= c.ema_trend else None
+        ema21 = close.ewm(span=c.ema_medium, adjust=False).mean()
+
+        latest = close.iloc[-1]
+        s = ema50.iloc[-1]
+        m = ema21.iloc[-1]
+        t = ema200.iloc[-1] if ema200 is not None and len(ema200) >= c.ema_trend else None
+
+        if t is None:
+            result.indicators["ema_trend"] = IndicatorSignal(
+                name="EMA Trend", value=0, signal=SignalStrength.NEUTRAL,
+                confidence=30, description="EMA200 data insufficient", weight=1.0,
+            )
+            result.key_levels["ema_medium"] = float(m)
+            result.key_levels["ema_slow"] = float(s)
+            return
+
+        # 3-level alignment: price vs EMA50, EMA50 vs EMA200
+        if latest > s > t:
+            signal = SignalStrength.STRONG_BUY
+            conf = 85
+            desc = "Full bullish: price > EMA50 > EMA200"
+        elif latest > s and s < t:
+            signal = SignalStrength.BUY
+            conf = 60
+            desc = "Emerging uptrend: price > EMA50, EMA50 < EMA200"
+        elif latest < s < t:
+            signal = SignalStrength.STRONG_SELL
+            conf = 85
+            desc = "Full bearish: price < EMA50 < EMA200"
+        elif latest < s and s > t:
+            signal = SignalStrength.SELL
+            conf = 60
+            desc = "Emerging downtrend: price < EMA50, EMA50 > EMA200"
+        else:
+            signal = SignalStrength.NEUTRAL
+            conf = 40
+            desc = "EMA alignment mixed"
+
+        # EMA50/200 crossover detection (recent 10 bars)
+        if len(ema50) >= 10 and ema200 is not None:
+            for lookback in range(2, min(11, len(ema50))):
+                prev_s = ema50.iloc[-lookback]
+                prev_t = ema200.iloc[-lookback]
+                if prev_s <= prev_t and s > t:
+                    desc += " | Golden cross (50/200)"
+                    conf = min(conf + 10, 95)
+                    break
+                elif prev_s >= prev_t and s < t:
+                    desc += " | Death cross (50/200)"
+                    conf = min(conf + 10, 95)
+                    break
+
+        normalized = 1.0 if signal == SignalStrength.STRONG_BUY else (
+            0.5 if signal == SignalStrength.BUY else (
+                -1.0 if signal == SignalStrength.STRONG_SELL else (
+                    -0.5 if signal == SignalStrength.SELL else 0.0)))
+
+        result.indicators["ema_trend"] = IndicatorSignal(
+            name="EMA Trend", value=normalized,
+            signal=signal, confidence=conf,
+            description=desc, weight=1.0,
+        )
+        result.key_levels["ema_trend"] = float(t)
+        result.key_levels["ema_medium"] = float(m)
+        result.key_levels["ema_slow"] = float(s)
+
+    def _compute_ema_4(self, df: pd.DataFrame, result: TechnicalAnalysis):
+        """Original 4-EMA (9/21/50/200) trend alignment analysis."""
         c = self.config
         close = df["Close"]
 
@@ -123,8 +207,6 @@ class TechnicalEngine:
         f, m, s = ema_fast.iloc[-1], ema_medium.iloc[-1], ema_slow.iloc[-1]
         t = ema_trend.iloc[-1] if ema_trend is not None and len(ema_trend) >= c.ema_trend else None
 
-        # Bullish: price > fast > medium > slow > trend
-        # Bearish: price < fast < medium < slow < trend
         alignment_score = 0
         checks = [(latest, f), (f, m), (m, s)]
         if t is not None:
@@ -155,7 +237,6 @@ class TechnicalEngine:
             signal = SignalStrength.NEUTRAL
             conf = 40
 
-        # Check for EMA crossover (recent)
         crossover_desc = ""
         if len(ema_fast) >= 3:
             prev_f, prev_m = ema_fast.iloc[-2], ema_medium.iloc[-2]
@@ -187,10 +268,10 @@ class TechnicalEngine:
         gain = delta.where(delta > 0, 0.0)
         loss = (-delta).where(delta < 0, 0.0)
 
-        avg_gain = gain.ewm(alpha=1/c.rsi_period, min_periods=c.rsi_period).mean()
+        avg_gain = gain.ewm(alpha=1/c.rsi_period, min_periods=c.rsi_period, adjust=False).mean()
-        avg_loss = loss.ewm(alpha=1/c.rsi_period, min_periods=c.rsi_period).mean()
+        avg_loss = loss.ewm(alpha=1/c.rsi_period, min_periods=c.rsi_period, adjust=False).mean()
 
-        rs = avg_gain / avg_loss.replace(0, np.nan)
+        rs = avg_gain / avg_loss.replace(0, 1e-10)
         rsi = 100 - (100 / (1 + rs))
         rsi_val = rsi.iloc[-1] if not rsi.empty else 50.0
 
@@ -227,8 +308,8 @@ class TechnicalEngine:
         # Attach RSI series for divergence detection (dynamic attribute, filtered by _prefix)
         result._rsi_series = rsi  # type: ignore[attr-defined]
 
-    def _compute_macd_internal(self, df: pd.DataFrame, result: TechnicalAnalysis):
+    def _compute_macd(self, df: pd.DataFrame, result: TechnicalAnalysis):
-        """Compute MACD histogram internally for divergence detection (not a signal pillar)."""
+        """MACD computation — always runs for divergence; optionally a signal pillar."""
         c = self.config
         close = df["Close"]
 
@@ -240,13 +321,70 @@ class TechnicalEngine:
 
         result._macd_histogram = histogram  # type: ignore[attr-defined]
 
+        if not c.macd_as_pillar:
+            return
+
+        if len(histogram) < 3:
+            return
+
+        hist_now = histogram.iloc[-1]
+        hist_prev = histogram.iloc[-2]
+        hist_prev2 = histogram.iloc[-3]
+
+        if pd.isna(hist_now) or pd.isna(hist_prev):
+            return
+
+        # Histogram zero-line crossover
+        crossed_up = hist_prev <= 0 < hist_now
+        crossed_down = hist_prev >= 0 > hist_now
+        # Momentum acceleration
+        accel_up = hist_now > 0 and hist_now > hist_prev > 0
+        accel_down = hist_now < 0 and hist_now < hist_prev < 0
+        # Momentum fading
+        fading_up = hist_now > 0 and hist_now < hist_prev
+        fading_down = hist_now < 0 and hist_now > hist_prev
+
+        # Normalize histogram relative to recent range for confidence
+        hist_abs = histogram.abs()
+        if len(hist_abs) >= 20:
+            hist_pct = (abs(hist_now) / hist_abs.iloc[-20:].mean()) if hist_abs.iloc[-20:].mean() > 0 else 1.0
+        else:
+            hist_pct = 1.0
+
+        if crossed_up or accel_up:
+            signal = SignalStrength.STRONG_BUY if accel_up else SignalStrength.BUY
+            conf = min(80, 55 + hist_pct * 10)
+            desc = "MACD histogram" + (" accelerating ↑" if accel_up else " crossed above zero")
+        elif crossed_down or accel_down:
+            signal = SignalStrength.STRONG_SELL if accel_down else SignalStrength.SELL
+            conf = min(80, 55 + hist_pct * 10)
+            desc = "MACD histogram" + (" accelerating ↓" if accel_down else " crossed below zero")
+        elif fading_up:
+            signal = SignalStrength.NEUTRAL
+            conf = 35
+            desc = f"MACD histogram positive but fading"
+        elif fading_down:
+            signal = SignalStrength.NEUTRAL
+            conf = 35
+            desc = f"MACD histogram negative but fading"
+        else:
+            signal = SignalStrength.NEUTRAL
+            conf = 30
+            desc = f"MACD histogram flat"
+
+        result.indicators["macd"] = IndicatorSignal(
+            name="MACD", value=float(hist_now),
+            signal=signal, confidence=conf,
+            description=desc, weight=1.5,
+        )
+
     def _compute_bollinger(self, df: pd.DataFrame, result: TechnicalAnalysis):
         """Bollinger Bands position and squeeze detection."""
         c = self.config
         close = df["Close"]
 
         sma = close.rolling(c.bb_period).mean()
-        std = close.rolling(c.bb_period).std()
+        std = close.rolling(c.bb_period).std(ddof=0)
         upper = sma + c.bb_std * std
         lower = sma - c.bb_std * std
 
@@ -308,67 +446,6 @@ class TechnicalEngine:
         result.key_levels["bb_lower"] = lower_val
         result.key_levels["bb_mid"] = sma_val
 
-    def _compute_stochastic(self, df: pd.DataFrame, result: TechnicalAnalysis):
-        """Stochastic oscillator with crossover signals."""
-        c = self.config
-        high = df["High"]
-        low = df["Low"]
-        close = df["Close"]
-
-        lowest_low = low.rolling(c.stoch_k).min()
-        highest_high = high.rolling(c.stoch_k).max()
-
-        denom = highest_high - lowest_low
-        denom = denom.replace(0, np.nan)
-
-        k_line = 100 * (close - lowest_low) / denom
-        d_line = k_line.rolling(c.stoch_d).mean()
-
-        k_val = k_line.iloc[-1] if not k_line.empty else 50.0
-        d_val = d_line.iloc[-1] if not d_line.empty else 50.0
-
-        if pd.isna(k_val):
-            k_val = 50.0
-        if pd.isna(d_val):
-            d_val = 50.0
-
-        # Crossover detection
-        crossover = ""
-        if len(k_line) >= 2 and len(d_line) >= 2:
-            prev_k = k_line.iloc[-2]
-            prev_d = d_line.iloc[-2]
-            if not pd.isna(prev_k) and not pd.isna(prev_d):
-                if prev_k <= prev_d and k_val > d_val:
-                    crossover = " | Bullish %K/%D cross"
-                elif prev_k >= prev_d and k_val < d_val:
-                    crossover = " | Bearish %K/%D cross"
-
-        if k_val >= c.stoch_overbought and d_val >= c.stoch_overbought:
-            signal = SignalStrength.SELL
-            conf = 60
-            desc = f"Stoch %K={k_val:.0f} %D={d_val:.0f} — Overbought{crossover}"
-        elif k_val <= c.stoch_oversold and d_val <= c.stoch_oversold:
-            signal = SignalStrength.BUY
-            conf = 60
-            desc = f"Stoch %K={k_val:.0f} %D={d_val:.0f} — Oversold{crossover}"
-        elif crossover and "Bullish" in crossover:
-            signal = SignalStrength.BUY
-            conf = 55
-            desc = f"Stoch %K={k_val:.0f} %D={d_val:.0f}{crossover}"
-        elif crossover and "Bearish" in crossover:
-            signal = SignalStrength.SELL
-            conf = 55
-            desc = f"Stoch %K={k_val:.0f} %D={d_val:.0f}{crossover}"
-        else:
-            signal = SignalStrength.NEUTRAL
-            conf = 30
-            desc = f"Stoch %K={k_val:.0f} %D={d_val:.0f} — Neutral"
-
-        result.indicators["stochastic"] = IndicatorSignal(
-            name="Stochastic", value=k_val, signal=signal,
-            confidence=conf, description=desc, weight=2.5,
-        )
-
     def _compute_adx(self, df: pd.DataFrame, result: TechnicalAnalysis):
         """ADX trend strength measurement."""
         c = self.config
@@ -389,12 +466,12 @@ class TechnicalEngine:
         plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0.0)
         minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0.0)
 
-        atr = tr.ewm(alpha=1/c.adx_period, min_periods=c.adx_period).mean()
+        atr = tr.ewm(alpha=1/c.adx_period, min_periods=c.adx_period, adjust=False).mean()
-        plus_di = 100 * (plus_dm.ewm(alpha=1/c.adx_period, min_periods=c.adx_period).mean() / atr)
+        plus_di = 100 * (plus_dm.ewm(alpha=1/c.adx_period, min_periods=c.adx_period, adjust=False).mean() / atr)
-        minus_di = 100 * (minus_dm.ewm(alpha=1/c.adx_period, min_periods=c.adx_period).mean() / atr)
+        minus_di = 100 * (minus_dm.ewm(alpha=1/c.adx_period, min_periods=c.adx_period, adjust=False).mean() / atr)
 
         dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, np.nan)
-        adx = dx.ewm(alpha=1/c.adx_period, min_periods=c.adx_period).mean()
+        adx = dx.ewm(alpha=1/c.adx_period, min_periods=c.adx_period, adjust=False).mean()
 
         adx_val = adx.iloc[-1] if not adx.empty and not pd.isna(adx.iloc[-1]) else 0.0
         plus_di_last = plus_di.iloc[-1] if not plus_di.empty else np.nan
@@ -439,7 +516,7 @@ class TechnicalEngine:
         tr3 = (low - close.shift()).abs()
         tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
 
-        atr = tr.ewm(alpha=1/c.atr_period, min_periods=c.atr_period).mean()
+        atr = tr.ewm(alpha=1/c.atr_period, min_periods=c.atr_period, adjust=False).mean()
         atr_val = atr.iloc[-1] if not atr.empty else 0.0
         if pd.isna(atr_val):
             atr_val = 0.0
@@ -473,91 +550,6 @@ class TechnicalEngine:
             weight=0.0,  # ATR is context, not a directional signal
         )
 
-    def _compute_ichimoku(self, df: pd.DataFrame, result: TechnicalAnalysis):
-        """Ichimoku Cloud analysis."""
-        c = self.config
-        high, low, close = df["High"], df["Low"], df["Close"]
-
-        if len(df) < c.ichimoku_senkou_b + 26:
-            return
-
-        # Tenkan-sen (Conversion Line)
-        tenkan = (high.rolling(c.ichimoku_tenkan).max() +
-                  low.rolling(c.ichimoku_tenkan).min()) / 2
-
-        # Kijun-sen (Base Line)
-        kijun = (high.rolling(c.ichimoku_kijun).max() +
-                 low.rolling(c.ichimoku_kijun).min()) / 2
-
-        latest = close.iloc[-1]
-        tenkan_val = tenkan.iloc[-1]
-        kijun_val = kijun.iloc[-1]
-
-        # Current (unshifted) cloud
-        sa = (tenkan.iloc[-1] + kijun.iloc[-1]) / 2
-        sb_raw = (high.rolling(c.ichimoku_senkou_b).max() +
-                  low.rolling(c.ichimoku_senkou_b).min()) / 2
-        sb = sb_raw.iloc[-1]
-
-        if pd.isna(sa) or pd.isna(sb) or pd.isna(tenkan_val) or pd.isna(kijun_val):
-            return
-
-        cloud_top = max(sa, sb)
-        cloud_bottom = min(sa, sb)
-        cloud_is_green = sa > sb
-
-        signals = []
-        score = 0
-
-        if latest > cloud_top:
-            signals.append("Price ABOVE cloud")
-            score += 2
-        elif latest < cloud_bottom:
-            signals.append("Price BELOW cloud")
-            score -= 2
-        else:
-            signals.append("Price IN cloud (indecision)")
-
-        if tenkan_val > kijun_val:
-            signals.append("TK cross bullish")
-            score += 1
-        elif tenkan_val < kijun_val:
-            signals.append("TK cross bearish")
-            score -= 1
-
-        if cloud_is_green:
-            signals.append("Green cloud")
-            score += 1
-        else:
-            signals.append("Red cloud")
-            score -= 1
-
-        if score >= 3:
-            signal = SignalStrength.STRONG_BUY
-            conf = 75
-        elif score >= 1:
-            signal = SignalStrength.BUY
-            conf = 55
-        elif score <= -3:
-            signal = SignalStrength.STRONG_SELL
-            conf = 75
-        elif score <= -1:
-            signal = SignalStrength.SELL
-            conf = 55
-        else:
-            signal = SignalStrength.NEUTRAL
-            conf = 35
-
-        result.indicators["ichimoku"] = IndicatorSignal(
-            name="Ichimoku", value=float(score), signal=signal,
-            confidence=conf,
-            description=f"Ichimoku [{score}/4]: {' | '.join(signals)}",
-            weight=0.3,
-        )
-
-        result.key_levels["ichimoku_cloud_top"] = cloud_top
-        result.key_levels["ichimoku_cloud_bottom"] = cloud_bottom
-
     def _detect_divergences(self, df: pd.DataFrame, result: TechnicalAnalysis):
         """
         Detect RSI and MACD divergences (powerful reversal signals).
@@ -615,7 +607,7 @@ class TechnicalEngine:
                 name="Divergence", value=len(divergences),
                 signal=signal, confidence=70,
                 description=" | ".join(divergences),
-                weight=1.0,
+                weight=1.5,
             )
             # Note: divergences are NOT added to pattern_signals to avoid
             # duplicate reason collection (the indicator entry covers them).
@@ -735,90 +727,96 @@ class TechnicalEngine:
                 name="Candlestick", value=float(bullish - bearish),
                 signal=signal, confidence=conf,
                 description=" | ".join(patterns),
-                weight=1.5,
+                weight=1.0,
             )
             result.pattern_signals.extend(patterns)
 
-    def _compute_support_resistance(self, df: pd.DataFrame, result: TechnicalAnalysis):
-        """Calculate pivot points and key S/R levels."""
-        if len(df) < 2:
-            return
-
-        # Use previous bar for pivot calculation
-        prev_h = df["High"].iloc[-2]
-        prev_l = df["Low"].iloc[-2]
-        prev_c = df["Close"].iloc[-2]
-
-        pivot = (prev_h + prev_l + prev_c) / 3
-        r1 = 2 * pivot - prev_l
-        s1 = 2 * pivot - prev_h
-        r2 = pivot + (prev_h - prev_l)
-        s2 = pivot - (prev_h - prev_l)
-        r3 = prev_h + 2 * (pivot - prev_l)
-        s3 = prev_l - 2 * (prev_h - pivot)
-
-        result.key_levels.update({
-            "pivot": pivot,
-            "r1": r1, "r2": r2, "r3": r3,
-            "s1": s1, "s2": s2, "s3": s3,
-        })
-
-        # Signal based on price position relative to pivot
-        latest = df["Close"].iloc[-1]
-        if latest > r1:
-            desc = f"Price above R1 ({r1:.5f}), next target R2 ({r2:.5f})"
-        elif latest > pivot:
-            desc = f"Price above Pivot ({pivot:.5f}), testing R1 ({r1:.5f})"
-        elif latest > s1:
-            desc = f"Price below Pivot ({pivot:.5f}), support at S1 ({s1:.5f})"
-        else:
-            desc = f"Price below S1 ({s1:.5f}), support at S2 ({s2:.5f})"
-
-        result.indicators["pivot"] = IndicatorSignal(
-            name="Pivot Points", value=pivot,
-            signal=SignalStrength.BUY if latest > pivot else SignalStrength.SELL,
-            confidence=45, description=desc, weight=0.8,
-        )
-
-    def _compute_volume_analysis(self, df: pd.DataFrame, result: TechnicalAnalysis):
-        """Volume analysis (limited on Forex but still useful as a filter)."""
-        c = self.config
-        vol = df.get("Volume")
-        if vol is None or vol.sum() == 0:
-            return
-
-        vol_ma = vol.rolling(c.volume_ma_period).mean()
-        current_vol = vol.iloc[-1]
-        avg_vol = vol_ma.iloc[-1]
-
-        if pd.isna(avg_vol) or avg_vol == 0:
-            return
-
-        vol_ratio = current_vol / avg_vol
-
-        if vol_ratio > 2.0:
-            desc = f"Volume {vol_ratio:.1f}x average — HIGH ACTIVITY"
-            conf = 55
-        elif vol_ratio > 1.3:
-            desc = f"Volume {vol_ratio:.1f}x average — above normal"
-            conf = 45
-        elif vol_ratio < 0.5:
-            desc = f"Volume {vol_ratio:.1f}x average — LOW (signals less reliable)"
-            conf = 25
-        else:
-            desc = f"Volume {vol_ratio:.1f}x average — normal"
-            conf = 35
-
-        result.indicators["volume"] = IndicatorSignal(
-            name="Volume", value=vol_ratio,
-            signal=SignalStrength.NEUTRAL,
-            confidence=conf, description=desc, weight=0.0,
-        )
-
     # ══════════════════════════════════════════════════════════════
     #  MARKET REGIME & COMPOSITE SCORING
     # ══════════════════════════════════════════════════════════════
 
+    def _reinterpret_rsi_for_regime(self, result: TechnicalAnalysis):
+        """Reinterpret RSI signal based on market regime — called after regime classification."""
+        rsi_ind = result.indicators.get("rsi")
+        if not rsi_ind:
+            return
+
+        rsi_val = rsi_ind.value
+        regime = result.market_regime
+
+        if regime in ("trending_up", "trending"):
+            if 38 <= rsi_val <= 55:
+                signal = SignalStrength.STRONG_BUY
+                conf = 75
+                desc = f"RSI {rsi_val:.1f} — Pullback zone in uptrend (ideal entry)"
+            elif 30 <= rsi_val < 38:
+                signal = SignalStrength.BUY
+                conf = 60
+                desc = f"RSI {rsi_val:.1f} — Deep pullback in uptrend"
+            elif 55 < rsi_val <= 65:
+                signal = SignalStrength.BUY
+                conf = 50
+                desc = f"RSI {rsi_val:.1f} — Momentum ok in uptrend"
+            elif rsi_val > 65:
+                signal = SignalStrength.NEUTRAL
+                conf = 30
+                desc = f"RSI {rsi_val:.1f} — Extended in uptrend, wait for pullback"
+            elif rsi_val < 30:
+                signal = SignalStrength.NEUTRAL
+                conf = 25
+                desc = f"RSI {rsi_val:.1f} — Oversold in uptrend, possible reversal"
+            else:
+                return
+        elif regime == "trending_down":
+            if 45 <= rsi_val <= 62:
+                signal = SignalStrength.STRONG_SELL
+                conf = 75
+                desc = f"RSI {rsi_val:.1f} — Bounce zone in downtrend (ideal short)"
+            elif 62 < rsi_val <= 70:
+                signal = SignalStrength.SELL
+                conf = 60
+                desc = f"RSI {rsi_val:.1f} — Deep bounce in downtrend"
+            elif 35 <= rsi_val < 45:
+                signal = SignalStrength.SELL
+                conf = 50
+                desc = f"RSI {rsi_val:.1f} — Momentum ok in downtrend"
+            elif rsi_val < 35:
+                signal = SignalStrength.NEUTRAL
+                conf = 30
+                desc = f"RSI {rsi_val:.1f} — Extended in downtrend, wait for bounce"
+            elif rsi_val > 70:
+                signal = SignalStrength.NEUTRAL
+                conf = 25
+                desc = f"RSI {rsi_val:.1f} — Overbought in downtrend, possible reversal"
+            else:
+                return
+        else:  # ranging
+            if rsi_val >= 70:
+                signal = SignalStrength.SELL
+                conf = 70
+                desc = f"RSI {rsi_val:.1f} — Overbought in range"
+            elif rsi_val <= 30:
+                signal = SignalStrength.BUY
+                conf = 70
+                desc = f"RSI {rsi_val:.1f} — Oversold in range"
+            elif rsi_val >= 60:
+                signal = SignalStrength.SELL
+                conf = 45
+                desc = f"RSI {rsi_val:.1f} — Upper range zone"
+            elif rsi_val <= 40:
+                signal = SignalStrength.BUY
+                conf = 45
+                desc = f"RSI {rsi_val:.1f} — Lower range zone"
+            else:
+                signal = SignalStrength.NEUTRAL
+                conf = 30
+                desc = f"RSI {rsi_val:.1f} — Mid-range, no edge"
+
+        result.indicators["rsi"] = IndicatorSignal(
+            name="RSI", value=rsi_val, signal=signal,
+            confidence=conf, description=desc, weight=rsi_ind.weight,
+        )
+
     def _classify_regime(self, df: pd.DataFrame, result: TechnicalAnalysis):
         """Classify the market regime (trending vs ranging)."""
         adx_sig = result.indicators.get("adx")
@@ -840,26 +838,29 @@ class TechnicalEngine:
 
         Weighted average of all indicator signals, adjusted for:
         - Market regime (trend indicators weighted more in trends)
-        - Divergence boost (divergences override in ranging markets)
         - Confidence scaling
         """
         total_score = 0.0
         total_weight = 0.0
         total_confidence = 0.0
 
-        _trending = {
+        # Use override weights if provided, else defaults
-            "ema_trend": 2.0, "rsi": 1.5, "bollinger": 0.8,
+        if self.config.regime_weights_override:
-            "candlestick": 1.5, "divergence": 1.0,
+            regime_weights = self.config.regime_weights_override
-        }
+        else:
-        regime_weights = {
+            _trending = {
-            "trending_up": _trending,
+                "ema_trend": 2.0, "rsi": 1.5, "bollinger": 0.8,
-            "trending_down": _trending,
+                "candlestick": 1.0, "divergence": 1.5,
-            "trending": _trending,
+            }
-            "ranging": {
+            regime_weights = {
-                "ema_trend": 0.5, "rsi": 2.0, "bollinger": 2.0,
+                "trending_up": _trending,
-                "candlestick": 1.5, "divergence": 1.5,
+                "trending_down": _trending,
-            },
+                "trending": _trending,
-        }
+                "ranging": {
+                    "ema_trend": 0.5, "rsi": 2.0, "bollinger": 2.0,
+                    "candlestick": 1.0, "divergence": 2.0,
+                },
+            }
 
         active_regime_weights = regime_weights.get(result.market_regime, {})
 
@@ -871,7 +872,7 @@ class TechnicalEngine:
             regime_mult = active_regime_weights.get(name, 1.0)
             weight *= regime_mult
 
-            signal_score = ind.signal.value  # -2 to +2
+            signal_score = ind.signal.value
             weighted_score = signal_score * weight * (ind.confidence / 100.0)
 
             total_score += weighted_score
@@ -879,23 +880,21 @@ class TechnicalEngine:
             total_confidence += ind.confidence * weight
 
         if total_weight > 0:
-            normalized_score = (total_score / total_weight) * 50  # scale to -100..+100
+            normalized_score = (total_score / total_weight) * 50
             avg_confidence = total_confidence / total_weight
         else:
             normalized_score = 0
             avg_confidence = 0
 
-        # Clamp
         normalized_score = max(-100, min(100, normalized_score))
 
-        # Determine composite signal
+        if normalized_score >= 45:
-        if normalized_score >= 40:
             result.composite_signal = SignalStrength.STRONG_BUY
-        elif normalized_score >= 15:
+        elif normalized_score >= 20:
             result.composite_signal = SignalStrength.BUY
-        elif normalized_score <= -40:
+        elif normalized_score <= -45:
             result.composite_signal = SignalStrength.STRONG_SELL
-        elif normalized_score <= -15:
+        elif normalized_score <= -20:
             result.composite_signal = SignalStrength.SELL
         else:
             result.composite_signal = SignalStrength.NEUTRAL

signal_aggregator.py

@@ -110,9 +110,13 @@ class SignalAggregator:
             timeframes.append(bt.regime_filter_timeframe)
 
         dfs: Dict[Timeframe, pd.DataFrame] = {}
+        stale_tfs = []
         for tf in timeframes:
             try:
-                dfs[tf] = self.data_provider.fetch_ohlcv(symbol, tf)
+                df = self.data_provider.fetch_ohlcv(symbol, tf)
+                dfs[tf] = df
+                if not self.data_provider._is_data_fresh(df, symbol, tf):
+                    stale_tfs.append(tf.value)
             except Exception as e:
                 logger.warning(f"Fetch failed for {symbol} {tf.value}: {e}")
 
@@ -123,6 +127,14 @@ class SignalAggregator:
             use_news=True,
         )
 
+        if stale_tfs:
+            report.warnings.append(f"STALE DATA on {', '.join(stale_tfs)} — signal may be unreliable")
+            if self.config.primary_timeframe.value in stale_tfs:
+                report.final_signal = SignalStrength.NEUTRAL
+                report.signal_quality = "F"
+                report.trade_setup = None
+                report.warnings.append("Primary timeframe stale — signal suppressed")
+
         if bt.regime_filter:
             self._apply_regime_filter_live(report, dfs)
 
@@ -206,12 +218,12 @@ class SignalAggregator:
         minus_dm = -low.diff()
         plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0.0)
         minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0.0)
-        atr_s = tr.ewm(alpha=1/period, min_periods=period).mean()
+        atr_s = tr.ewm(alpha=1/period, min_periods=period, adjust=False).mean()
-        plus_di = 100 * (plus_dm.ewm(alpha=1/period, min_periods=period).mean() / atr_s)
+        plus_di = 100 * (plus_dm.ewm(alpha=1/period, min_periods=period, adjust=False).mean() / atr_s)
-        minus_di = 100 * (minus_dm.ewm(alpha=1/period, min_periods=period).mean() / atr_s)
+        minus_di = 100 * (minus_dm.ewm(alpha=1/period, min_periods=period, adjust=False).mean() / atr_s)
         denom = (plus_di + minus_di).replace(0, float("nan"))
         dx = 100 * (plus_di - minus_di).abs() / denom
-        adx = dx.ewm(alpha=1/period, min_periods=period).mean().dropna()
+        adx = dx.ewm(alpha=1/period, min_periods=period, adjust=False).mean().dropna()
         if adx.empty:
             return None
         return float(adx.iloc[-1])
@@ -308,9 +320,32 @@ class SignalAggregator:
         # ── 6. Final signal ────────────────────────────────────────
         self._compute_final_signal(report)
 
+        # ── 6b. Session filter (works for both live and backtest) ─
+        if self.config.backtest.session_filter:
+            try:
+                bar_hour = pd.Timestamp(report.timestamp).hour
+                if bar_hour in self.config.backtest.blocked_hours_utc:
+                    report.final_signal = SignalStrength.NEUTRAL
+                    report.signal_quality = "F"
+                    report.warnings.append(f"SESSION FILTER: {bar_hour:02}:00 UTC blocked")
+            except Exception:
+                pass
+
         # ── 7. Trade setup for actionable signals ──────────────────
         if report.signal_quality in ("A+", "A", "B", "C"):
-            report.trade_setup = self._generate_trade_setup(report, pip_value)
+            is_sell_direction = (
+                report.final_signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL)
+                or (report.final_signal == SignalStrength.NEUTRAL and report.final_score < 0)
+            )
+            if is_sell_direction and self.config.backtest.sell_filter:
+                if report.display_name in self.config.backtest.sell_allowed_pairs:
+                    report.trade_setup = self._generate_trade_setup(report, pip_value)
+                elif self._forex_sell_qualifies(report):
+                    report.trade_setup = self._generate_trade_setup(report, pip_value)
+                else:
+                    report.trade_setup = None
+            else:
+                report.trade_setup = self._generate_trade_setup(report, pip_value)
 
         # ── 8. Reasons ─────────────────────────────────────────────
         self._collect_reasons(report)
@@ -319,6 +354,11 @@ class SignalAggregator:
 
     # ── Internals ──────────────────────────────────────────────────
 
+    def _forex_sell_qualifies(self, report: SignalReport) -> bool:
+        """Always returns False — forex SELL is structurally unprofitable.
+        0/4 trades won even under best conditions (trending_down + MTF + ADX>=25 + RSI<=50)."""
+        return False
+
     def _check_mtf_agreement(self, report: SignalReport) -> bool:
         signals = list(report.mtf_signals.values())
         if len(signals) < 2:
@@ -398,16 +438,90 @@ class SignalAggregator:
                     )
                     confidence -= 10
 
+            atr = primary.key_levels.get("atr", 0)
+            if atr > 0 and score != 0:
+                is_buy_signal = score > 0
+                if is_buy_signal:
+                    swing_lows = sorted(
+                        v for k, v in primary.key_levels.items()
+                        if k.startswith("swing_low_")
+                    )
+                    if swing_lows and report.current_price - swing_lows[0] > 2.5 * atr:
+                        confidence -= 15
+                        report.warnings.append("TREND EXHAUSTION — price extended >2.5 ATR past swing low")
+                else:
+                    swing_highs = sorted(
+                        (v for k, v in primary.key_levels.items()
+                         if k.startswith("swing_high_")),
+                        reverse=True,
+                    )
+                    if swing_highs and swing_highs[0] - report.current_price > 2.5 * atr:
+                        confidence -= 15
+                        report.warnings.append("TREND EXHAUSTION — price extended >2.5 ATR past swing high")
+
+            ema_medium = primary.key_levels.get("ema_medium")
+            if atr > 0 and ema_medium and report.market_regime.startswith("trending"):
+                pullback_dist = abs(report.current_price - ema_medium) / atr
+                if pullback_dist > 2.0:
+                    confidence -= 20
+                    report.warnings.append(
+                        f"NO PULLBACK — price {pullback_dist:.1f} ATR from EMA21, chasing risk"
+                    )
+                elif pullback_dist > 1.5:
+                    confidence -= 10
+                    report.warnings.append(
+                        f"Weak pullback — price {pullback_dist:.1f} ATR from EMA21"
+                    )
+
+            if report.market_regime.startswith("trending"):
+                rsi_ind = primary.indicators.get("rsi")
+                bb_ind = primary.indicators.get("bollinger")
+
+                if rsi_ind:
+                    rsi_val = rsi_ind.value
+                    if report.market_regime in ("trending_up", "trending") and score > 0:
+                        if rsi_val > 65:
+                            confidence -= 15
+                            report.warnings.append(
+                                f"Extended entry — RSI {rsi_val:.0f} in uptrend (prefer pullback to 40-55)"
+                            )
+                        elif 38 <= rsi_val <= 55:
+                            confidence += 5
+                    elif report.market_regime == "trending_down" and score < 0:
+                        if rsi_val < 35:
+                            confidence -= 15
+                            report.warnings.append(
+                                f"Extended entry — RSI {rsi_val:.0f} in downtrend (prefer bounce to 45-62)"
+                            )
+                        elif 45 <= rsi_val <= 62:
+                            confidence += 5
+
+                if bb_ind:
+                    bb_pos = bb_ind.value
+                    if report.market_regime in ("trending_up", "trending") and score > 0:
+                        if bb_pos > 0.85:
+                            confidence -= 10
+                        elif bb_pos < 0.4:
+                            confidence += 5
+                    elif report.market_regime == "trending_down" and score < 0:
+                        if bb_pos < 0.15:
+                            confidence -= 10
+                        elif bb_pos > 0.6:
+                            confidence += 5
+
         confidence = max(0.0, min(95.0, confidence))
 
         # Final signal
-        if score >= 35 and confidence >= 60:
+        if score > 38:
+            confidence -= 20
+            report.warnings.append("EXHAUSTION: score > 38 — trend overextended")
+        if score >= 40 and confidence >= 60:
             report.final_signal = SignalStrength.STRONG_BUY
-        elif score >= 12 and confidence >= 45:
+        elif score >= 30 and confidence >= 55:
             report.final_signal = SignalStrength.BUY
-        elif score <= -35 and confidence >= 60:
+        elif score <= -40 and confidence >= 60:
             report.final_signal = SignalStrength.STRONG_SELL
-        elif score <= -12 and confidence >= 45:
+        elif score <= -30 and confidence >= 55:
             report.final_signal = SignalStrength.SELL
         else:
             report.final_signal = SignalStrength.NEUTRAL
@@ -415,17 +529,29 @@ class SignalAggregator:
         report.final_score = score
         report.final_confidence = confidence
 
+        # SELL suppression (pair-aware)
+        sell_suppressed = False
+        if self.config.backtest.sell_filter and report.final_signal in (
+            SignalStrength.SELL, SignalStrength.STRONG_SELL
+        ):
+            if report.display_name not in self.config.backtest.sell_allowed_pairs:
+                report.final_signal = SignalStrength.NEUTRAL
+                report.warnings.append(f"SELL SUPPRESSED for {report.display_name}")
+                sell_suppressed = True
+
         # Quality grade
         abs_score = abs(score)
-        if abs_score >= 45 and confidence >= 70 and report.mtf_agreement:
+        if sell_suppressed:
+            report.signal_quality = "F"
+        elif abs_score >= 45 and confidence >= 70 and report.mtf_agreement:
             report.signal_quality = "A+"
-        elif abs_score >= 30 and confidence >= 60 and report.mtf_agreement:
+        elif abs_score >= 38 and confidence >= 60 and report.mtf_agreement:
             report.signal_quality = "A"
-        elif abs_score >= 20 and confidence >= 50:
+        elif abs_score >= 33 and confidence >= 55:
             report.signal_quality = "B"
-        elif abs_score >= 12 and confidence >= 40:
+        elif abs_score >= 30 and confidence >= 50:
             report.signal_quality = "C"
-        elif abs_score >= 8:
+        elif abs_score >= 15:
             report.signal_quality = "D"
         else:
             report.signal_quality = "F"
@@ -450,7 +576,15 @@ class SignalAggregator:
         if atr == 0:
             return None
 
-        is_buy = report.final_signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY)
+        min_sl_pips = 8.0 if pip_value < 0.01 else 80.0
+        min_sl_price = min_sl_pips * pip_value
+
+        if report.final_signal in (SignalStrength.BUY, SignalStrength.STRONG_BUY):
+            is_buy = True
+        elif report.final_signal in (SignalStrength.SELL, SignalStrength.STRONG_SELL):
+            is_buy = False
+        else:
+            is_buy = report.final_score >= 0
         levels = report.primary_analysis.key_levels
         min_rr = self.config.signals.min_rr_ratio
 
@@ -467,8 +601,9 @@ class SignalAggregator:
                 sl_price = swing_lows[0] - atr * 1.0
             else:
                 sl_price = price - atr * 2.0
-            if price - sl_price < atr * 1.5:
+            min_sl = max(atr * 1.5, min_sl_price)
-                sl_price = price - atr * 1.5
+            if price - sl_price < min_sl:
+                sl_price = price - min_sl
 
             sl_distance = price - sl_price
 
@@ -496,8 +631,9 @@ class SignalAggregator:
                 sl_price = swing_highs[0] + atr * 1.0
             else:
                 sl_price = price + atr * 2.0
-            if sl_price - price < atr * 1.5:
+            min_sl = max(atr * 1.5, min_sl_price)
-                sl_price = price + atr * 1.5
+            if sl_price - price < min_sl:
+                sl_price = price + min_sl
 
             sl_distance = sl_price - price
 

telegram_bot.py

@@ -54,7 +54,7 @@ from telegram.ext import (
 
 from config import (
     PlatformConfig, SignalStrength, Timeframe,
-    EURUSD, USDJPY, GBPUSD, XAUUSD, PAIR_INFO,
+    EURUSD, GBPUSD, XAUUSD, PAIR_INFO,
 )
 from data_provider import ForexDataProvider
 from signal_aggregator import SignalAggregator, SignalReport
@@ -77,11 +77,10 @@ CHAT_ID = os.environ.get("TELEGRAM_CHAT_ID", "6025899022")
 
 # ── Pairs & buttons ───────────────────────────────────────────────────
 
-SCAN_PAIRS = [EURUSD, USDJPY, GBPUSD, XAUUSD]
+SCAN_PAIRS = [EURUSD, GBPUSD, XAUUSD]
 
 CORRELATION_GROUPS = {
     "usd_negative": {"pairs": {EURUSD, GBPUSD}, "label": "USD-weakness"},
-    "risk_sentiment": {"pairs": {XAUUSD, USDJPY}, "label": "Risk-sentiment"},
 }
 
 _WS_TO_PAIR = {
@@ -93,17 +92,14 @@ _WS_SYMBOLS = list(_WS_TO_PAIR.keys())
 _LEVEL_KEYS = (
     "swing_high_1", "swing_high_2", "swing_high_3",
     "swing_low_1", "swing_low_2", "swing_low_3",
-    "bb_upper", "bb_lower", "ema_trend",
+    "ema_trend",
 )
 
 SCAN_BUTTONS = [
     {"cb": "scan_eurusd_m15", "symbol": EURUSD, "tf": Timeframe.M15, "label": "EUR/USD 15M"},
     {"cb": "scan_eurusd_h1",  "symbol": EURUSD, "tf": Timeframe.H1,  "label": "EUR/USD 1H"},
     {"cb": "scan_eurusd_h4",  "symbol": EURUSD, "tf": Timeframe.H4,  "label": "EUR/USD 4H"},
-    {"cb": "scan_usdjpy_m15", "symbol": USDJPY, "tf": Timeframe.M15, "label": "USD/JPY 15M"},
+{"cb": "scan_gbpusd_m15", "symbol": GBPUSD, "tf": Timeframe.M15, "label": "GBP/USD 15M"},
-    {"cb": "scan_usdjpy_h1",  "symbol": USDJPY, "tf": Timeframe.H1,  "label": "USD/JPY 1H"},
-    {"cb": "scan_usdjpy_h4",  "symbol": USDJPY, "tf": Timeframe.H4,  "label": "USD/JPY 4H"},
-    {"cb": "scan_gbpusd_m15", "symbol": GBPUSD, "tf": Timeframe.M15, "label": "GBP/USD 15M"},
     {"cb": "scan_gbpusd_h1",  "symbol": GBPUSD, "tf": Timeframe.H1,  "label": "GBP/USD 1H"},
     {"cb": "scan_gbpusd_h4",  "symbol": GBPUSD, "tf": Timeframe.H4,  "label": "GBP/USD 4H"},
     {"cb": "scan_xauusd_m15", "symbol": XAUUSD, "tf": Timeframe.M15, "label": "XAU/USD 15M"},
@@ -156,12 +152,12 @@ _QUALITY_EMOJI = {
     "A+": "🏆", "A": "🥇", "B": "🥈", "C": "🥉", "D": "⚠️", "F": "❌",
 }
 
-_ARROW = {
+_SIGNAL_TAG = {
-    SignalStrength.STRONG_BUY:  "⬆️",
+    SignalStrength.STRONG_BUY:  "Strong Buy",
-    SignalStrength.BUY:         "↗️",
+    SignalStrength.BUY:         "Bullish",
-    SignalStrength.NEUTRAL:     "➡️",
+    SignalStrength.NEUTRAL:     "Neutral",
-    SignalStrength.SELL:        "↘️",
+    SignalStrength.SELL:        "Bearish",
-    SignalStrength.STRONG_SELL: "⬇️",
+    SignalStrength.STRONG_SELL: "Strong Sell",
 }
 
 
@@ -180,9 +176,9 @@ def _mtf_line(report: SignalReport) -> str:
 
 
 def _indicator_line(name: str, ind) -> str:
-    arrow = _ARROW.get(ind.signal, "➡️")
+    tag = _SIGNAL_TAG.get(ind.signal, "Neutral")
     short = ind.description.split(" | ")[0] if " | " in ind.description else ind.description
-    return f"{arrow} *{ind.name}*: {short}"
+    return f"[{tag}] *{ind.name}*: {short}"
 
 
 def format_signal_message(report: SignalReport) -> str:
@@ -601,9 +597,11 @@ async def cmd_start(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
 async def cmd_help(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
     text = (
         "📖 *ForexBot2 Commands*\n\n"
-        "/start — Show scan buttons (4 pairs × 4 TFs)\n"
+        "/start — Show scan buttons\n"
         "/autoscan — Toggle auto-scan on/off\n"
         "/status — Bot status, WS connection, live prices\n"
+        "/cache — Show when each pair/TF was last fetched\n"
+        "/clearcache — Wipe data cache, force fresh fetch\n"
         "/help — This message\n\n"
         "*Auto-scan* fires at candle close: M5/M15/H1/H4.\n"
         "*WebSocket* monitors live prices and sends Level Break Alerts "
@@ -628,10 +626,10 @@ async def cmd_status(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None
         lines.append("❌ WebSocket: *disconnected*")
 
     if _last_prices:
-        lines.append("\n*Live Prices:*")
+        lines.append("\n*Live Prices (WebSocket):*")
-        for pair in SCAN_PAIRS:
+        for ws_sym, pair in _WS_TO_PAIR.items():
             price = _last_prices.get(pair)
-            display = PAIR_INFO.get(pair, {}).get("display", pair)
+            display = PAIR_INFO.get(pair, {}).get("display", ws_sym)
             if price is None:
                 lines.append(f"  {display}: waiting...")
             elif price < 10:
@@ -658,6 +656,44 @@ async def cmd_status(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None
     await update.message.reply_text("\n".join(lines), parse_mode=ParseMode.MARKDOWN)
 
 
+async def cmd_cache(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
+    dp = _get_shared_dp()
+    if not dp._cache_timestamps:
+        await update.message.reply_text("📭 Cache is empty — no data fetched yet.")
+        return
+
+    now = time.time()
+    lines = ["📦 *Data Cache Status*\n"]
+
+    for pair in SCAN_PAIRS:
+        display = PAIR_INFO.get(pair, {}).get("display", pair)
+        pair_lines = []
+        for tf in [Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4, Timeframe.D1]:
+            cache_key = f"{pair}_{tf.value}"
+            ts = dp._cache_timestamps.get(cache_key)
+            if ts is None:
+                continue
+            age_min = (now - ts) / 60
+            df = dp._cache.get(cache_key)
+            last_bar = df.index[-1].strftime("%H:%M") if df is not None and not df.empty else "?"
+            fresh = "✅" if age_min < dp.CANDLE_SECONDS.get(tf, 3600) / 60 else "⚠️"
+            pair_lines.append(f"  {tf.value:<5} {fresh} fetched `{age_min:.0f}m` ago | last bar `{last_bar}`")
+        if pair_lines:
+            lines.append(f"*{display}*")
+            lines.extend(pair_lines)
+
+    await update.message.reply_text("\n".join(lines), parse_mode=ParseMode.MARKDOWN)
+
+
+async def cmd_clearcache(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
+    dp = _get_shared_dp()
+    count = dp.clear_cache()
+    await update.message.reply_text(
+        f"🗑 Cache cleared ({count} entries wiped).\nNext scan will fetch fresh data from API.",
+        parse_mode=ParseMode.MARKDOWN,
+    )
+
+
 async def cmd_autoscan(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
     chat_id = update.effective_chat.id
 
@@ -725,6 +761,8 @@ async def _post_init(app: Application) -> None:
         ("start", "Show scan buttons"),
         ("autoscan", "Toggle auto-scan on/off"),
         ("status", "Bot status, WS connection, live prices"),
+        ("cache", "Show data cache age per pair/TF"),
+        ("clearcache", "Wipe data cache, force fresh fetch"),
         ("help", "All commands"),
     ])
 
@@ -792,6 +830,8 @@ def main() -> None:
     app.add_handler(CommandHandler("help", cmd_help))
     app.add_handler(CommandHandler("status", cmd_status))
     app.add_handler(CommandHandler("autoscan", cmd_autoscan))
+    app.add_handler(CommandHandler("cache", cmd_cache))
+    app.add_handler(CommandHandler("clearcache", cmd_clearcache))
     app.add_handler(CallbackQueryHandler(cb_scan, pattern="^scan_"))
 
     logger.info("Bot running — press Ctrl+C to stop")