Add M5 gates 5-6 and trailing-stop exits (+48R → +105R on 12mo)
Gates 5-6 in the M5 filter: block 'high' vol regime entries (chop transition — 61% WR, negative total while low/normal/extreme all positive) and block SELLs with RSI<25 (dip already extended, mean- reverts before target). 365d: 419→311 trades, +41→+48.4R, DD 11.2→8.0R. Replace the fixed RR-0.5 take-profit with a trailing stop: arm at +0.6R favorable excursion, trail 0.75R behind best price, stop levels update at bar close (conservative). The RR-0.5 target was the binding constraint — winners kept running after TP. Whole trail parameter grid (arm 0.4-1.2 × dist 0.5-1.25) lands +90-103R, a plateau not a spike; also +91.7R on the pre-gate 419-trade population. TP1 stays advisory; Telegram alerts include the arm price and trail distance. max_hold 200→300 bars (trailing exits need room; +10R). 12mo M5, same 311 trades: +105.0R total, PF 2.27, DD 7.9R, 61% WR, train/val +54.6/+50.4, every quarter positive.
This commit is contained in:
harwinder
+109
-6
@@ -1161,16 +1161,108 @@ def _build_indicator_breakdown(
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return breakdown, agree, oppose, neutral, total
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def _simulate_trailing(
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trades: List[Trade],
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opens_arr: np.ndarray,
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highs_arr: np.ndarray,
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lows_arr: np.ndarray,
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closes_arr: np.ndarray,
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valid_lens: np.ndarray,
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exit_times: List[List[pd.Timestamp]],
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spread: float,
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trail_arm_r: float,
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trail_dist_r: float,
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) -> None:
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"""Resolve trades with a trailing stop (per-trade CPU loop).
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Exit reasons: "SL" (original stop, full loss), "TRAIL" (trailed stop
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filled at/above entry region), "TIMEOUT".
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"""
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for i, t in enumerate(trades):
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n = int(valid_lens[i])
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if n == 0:
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t.exit_reason = "NO_DATA"
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continue
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o = opens_arr[i, :n]
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h = highs_arr[i, :n]
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l = lows_arr[i, :n]
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c = closes_arr[i, :n]
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buy = t.direction == "BUY"
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risk = abs(t.entry_price - t.sl)
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if risk <= 0:
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t.exit_reason = "NO_DATA"
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continue
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cur_sl = t.sl
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peak = t.entry_price
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exit_price = None
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exit_idx = n - 1
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reason = "TIMEOUT"
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for j in range(n):
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if np.isnan(h[j]):
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break
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hit = (l[j] <= cur_sl) if buy else (h[j] >= cur_sl)
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if hit:
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exit_price = cur_sl
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# Gap through the stop → filled at the open.
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if (buy and o[j] < cur_sl) or (not buy and o[j] > cur_sl):
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exit_price = o[j]
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reason = "SL" if cur_sl == t.sl else "TRAIL"
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exit_idx = j
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break
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# Bar closed: advance peak, then ratchet the stop for the
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# NEXT bar.
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fav = h[j] if buy else l[j]
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peak = max(peak, fav) if buy else min(peak, fav)
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exc = ((peak - t.entry_price) if buy
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else (t.entry_price - peak)) / risk
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if exc >= trail_arm_r:
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lvl = (peak - trail_dist_r * risk if buy
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else peak + trail_dist_r * risk)
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cur_sl = max(cur_sl, lvl) if buy else min(cur_sl, lvl)
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if exit_price is None:
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exit_price = float(c[exit_idx])
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t.exit_reason = reason
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t.bars_held = exit_idx + 1
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t.exit_price = float(exit_price)
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t.exit_time = exit_times[i][exit_idx]
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pnl = ((t.exit_price - t.entry_price) if buy
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else (t.entry_price - t.exit_price))
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t.r_multiple = (pnl - spread) / risk
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h_win = h[:t.bars_held]
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l_win = l[:t.bars_held]
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if buy:
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mfe = (np.nanmax(h_win) - t.entry_price) / risk
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mae = (t.entry_price - np.nanmin(l_win)) / risk
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else:
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mfe = (t.entry_price - np.nanmin(l_win)) / risk
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mae = (np.nanmax(h_win) - t.entry_price) / risk
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t.mfe_r = float(max(0.0, mfe))
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t.mae_r = float(max(0.0, mae))
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def simulate_trades(
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trades: List[Trade],
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sim_df: pd.DataFrame,
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max_bars: int = 200,
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spread: float = 0.0,
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trail_arm_r: float = 0.0,
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trail_dist_r: float = 0.75,
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) -> None:
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"""Walk forward bar-by-bar after each entry and resolve SL / TP1.
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"""Walk forward bar-by-bar after each entry and resolve the exit.
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Vectorized on GPU when CuPy is available: builds (n_trades × max_bars)
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matrices of post-entry highs/lows and finds the first hit per trade.
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Two exit modes:
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- Static (trail_arm_r <= 0): resolve SL / TP1, vectorized on GPU when
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CuPy is available.
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- Trailing (trail_arm_r > 0): TP1 is advisory. Once favorable
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excursion reaches trail_arm_r × risk, the stop trails
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trail_dist_r × risk behind the best price. Stop levels update at
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bar CLOSE (intra-bar sequence is unknowable), so a new stop can
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only fill from the next bar on — conservative.
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SL/TP-same-bar tie: SL assumed first (conservative).
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Gap-through fills: a bar *opening* beyond SL/TP fills at the open (worse
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@@ -1217,6 +1309,13 @@ def simulate_trades(
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closes_arr[i, :n] = sim_close[entry_pos:end_pos]
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exit_times.append(list(sim_index[entry_pos:end_pos]))
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if trail_arm_r > 0:
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_simulate_trailing(
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trades, opens_arr, highs_arr, lows_arr, closes_arr,
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valid_lens, exit_times, spread, trail_arm_r, trail_dist_r,
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)
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return
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# GPU vectorized: for each trade, scan its window for first SL/TP hit.
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sls = np.array([t.sl for t in trades], dtype=np.float64)[:, None]
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tps = np.array([t.tp1 for t in trades], dtype=np.float64)[:, None]
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@@ -1336,7 +1435,9 @@ class BacktestConfig:
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)
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warmup_bars: int = 250 # min bars before first signal (EMA200 warmup)
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cooldown_bars: int = 5 # min bars between trades on same pair
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max_hold_bars: int = 200 # cap simulation length per trade
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max_hold_bars: int = 300 # cap simulation length per trade
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# (300: trailing exits need room to run;
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# 200→300 adds ~10R on 12mo M5)
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use_cache: bool = True
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quality_filter: List[str] = field(
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default_factory=lambda: ["A+", "A", "B", "C"]
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@@ -1604,7 +1705,9 @@ def run_backtest(
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)
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t_sim = time.perf_counter()
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simulate_trades(trades, sim_df, max_bars=bt_cfg.max_hold_bars,
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spread=bt_cfg.spread)
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spread=bt_cfg.spread,
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trail_arm_r=platform_cfg.signals.trail_arm_r,
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trail_dist_r=platform_cfg.signals.trail_dist_r)
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logger.info(
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f"Trade simulation took {time.perf_counter() - t_sim:.2f}s "
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f"(GPU={'yes' if _GPU_AVAILABLE else 'no'})"
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@@ -1926,7 +2029,7 @@ def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:
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p.add_argument("--confirm-tfs", default="1h,4h", help="Comma-separated confirmation TFs")
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p.add_argument("--warmup-bars", type=int, default=250)
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p.add_argument("--cooldown-bars", type=int, default=5)
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p.add_argument("--max-hold-bars", type=int, default=200)
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p.add_argument("--max-hold-bars", type=int, default=300)
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p.add_argument("--swing-sr-lookback", type=int, default=0,
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help="Override TechnicalConfig.swing_sr_lookback (0 = use config default)")
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p.add_argument("--quality", default="A+,A,B,C", help="Comma-separated allowed grades")
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