feat(clmm): manage_clmm tool to recover orphaned LP positions; bin_count on get_pool_info

agents/adaptive_grid_trader/AGENT.md

@@ -2,7 +2,7 @@
 name: Adaptive Grid Trader
 description: Expert in multi-timeframe adaptive grid trading with safety-first order
   sizing, a configurable untraded reserve, and strict risk management
-agent_key: claude-acp:opus
+agent_key: claude-acp:sonnet
 tools:
 - get_market_data
 - get_portfolio_overview

agents/adaptive_grid_trader/routines/kalman_regime_check.py

@@ -0,0 +1,194 @@
+
+from pydantic import BaseModel, Field
+from telegram.ext import ContextTypes
+from config_manager import get_client
+from condor.reports import ReportBuilder
+import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import logging
+from datetime import datetime, timezone
+
+logger = logging.getLogger(__name__)
+
+CATEGORY = "Analysis"
+
+
+def _candle_minutes(interval: str) -> float:
+    """Parse '1m'→1, '1h'→60, '4h'→240, '1d'→1440."""
+    if interval.endswith("m"):
+        return float(interval[:-1])
+    elif interval.endswith("h"):
+        return float(interval[:-1]) * 60
+    elif interval.endswith("d"):
+        return float(interval[:-1]) * 1440
+    return 1.0
+
+
+class Config(BaseModel):
+    """Kalman filter regime detection — outputs regime signal and Kalman-sized grid params."""
+    connector_name: str = Field(default="bitget_perpetual", description="Exchange connector")
+    trading_pair: str = Field(default="BTC-USDT", description="Trading pair")
+    candle_interval: str = Field(default="1m", description="Candle interval (1m, 5m, 1h, etc.)")
+    lookback_candles: int = Field(default=60, description="Candles to fetch (60 × 1m = last 1 hour)")
+    q_level: float = Field(default=100.0, description="Process noise: level variance")
+    q_slope: float = Field(default=1.0, description="Process noise: slope variance")
+    r_obs: float = Field(default=40.0, description="Observation noise variance")
+    snr_trend_threshold: float = Field(default=0.3, description="SNR >= this → TRENDING")
+    snr_range_threshold: float = Field(default=0.1, description="SNR <= this → RANGING")
+    target_grid_hours: float = Field(default=8.0, description="Target grid lifetime (hours) for D sizing")
+
+
+async def run(config: Config, context: ContextTypes.DEFAULT_TYPE) -> str:
+    client = await get_client(context._chat_id, context=context)
+    if not client:
+        return "No server available"
+
+    result = await client.market_data.get_candles(
+        config.connector_name, config.trading_pair,
+        interval=config.candle_interval, max_records=config.lookback_candles
+    )
+    records = result if isinstance(result, list) else result.get("data", result.get("candles", []))
+    if not records or len(records) < 20:
+        return f"Insufficient candle data: got {len(records) if records else 0} records"
+
+    closes, timestamps = [], []
+    for r in records:
+        try:
+            closes.append(float(r["close"]))
+            ts = r.get("timestamp", r.get("time", 0))
+            if isinstance(ts, (int, float)) and ts > 0:
+                t = ts / 1000 if ts > 1e10 else ts
+                timestamps.append(datetime.fromtimestamp(t, tz=timezone.utc).strftime("%m-%d %H:%M"))
+            else:
+                timestamps.append(str(ts))
+        except (KeyError, TypeError, ValueError):
+            continue
+
+    if len(closes) < 20:
+        return f"Could not parse enough closes: got {len(closes)}"
+
+    prices = np.array(closes)
+    n = len(prices)
+    x_axis = timestamps[:n] if len(timestamps) == n else list(range(n))
+
+    # Local Linear Trend Kalman Filter
+    F = np.array([[1.0, 1.0], [0.0, 1.0]])
+    H = np.array([[1.0, 0.0]])
+    Q = np.diag([config.q_level, config.q_slope])
+    R = np.array([[config.r_obs]])
+    x = np.array([prices[0], 0.0])
+    P = np.eye(2) * 1e6
+
+    levels, slopes, innovations = [], [], []
+    for price in prices:
+        x_pred = F @ x
+        P_pred = F @ P @ F.T + Q
+        inn = price - float((H @ x_pred)[0])
+        S = float((H @ P_pred @ H.T + R)[0, 0])
+        K = (P_pred @ H.T / S).flatten()
+        x = x_pred + K * inn
+        P = (np.eye(2) - np.outer(K, H)) @ P_pred
+        levels.append(float(x[0]))
+        slopes.append(float(x[1]))
+        innovations.append(float(inn))
+
+    levels = np.array(levels)
+    slopes = np.array(slopes)
+    innovations = np.array(innovations)
+
+    window = min(24, max(n // 6, 4))
+    recent_slope = slopes[-1]
+    recent_price = prices[-1]
+    rms_inn = float(np.sqrt(np.mean(innovations[-window:] ** 2)))
+    sigma_inn = max(rms_inn, 1.0)
+    slope_pct = recent_slope / recent_price * 100
+    snr = abs(recent_slope) / sigma_inn
+
+    # D: scale by candle duration so grid width is timeframe-agnostic
+    candle_min = _candle_minutes(config.candle_interval)
+    grid_D = sigma_inn * float(np.sqrt(config.target_grid_hours * 60 / candle_min))
+
+    if n < 30:
+        regime, profile = "UNCERTAIN", "HOLD"
+    elif snr >= config.snr_trend_threshold:
+        regime = "TRENDING_UP" if recent_slope > 0 else "TRENDING_DOWN"
+        profile = "LONG" if recent_slope > 0 else "SHORT"
+    elif snr <= config.snr_range_threshold:
+        regime, profile = "RANGING", "TWO_SIDED"
+    else:
+        regime, profile = "UNCERTAIN", "HOLD"
+
+    if regime == "TRENDING_UP":
+        g_start = recent_price - grid_D
+        g_end   = recent_price + 3 * grid_D
+        g_limit = recent_price - 1.5 * grid_D
+    elif regime == "TRENDING_DOWN":
+        g_start = recent_price - 3 * grid_D
+        g_end   = recent_price + grid_D
+        g_limit = recent_price + 1.5 * grid_D
+    else:
+        g_start = recent_price - 1.5 * grid_D
+        g_end   = recent_price + 1.5 * grid_D
+        g_limit = None
+
+    fig = make_subplots(
+        rows=3, cols=1, shared_xaxes=True,
+        subplot_titles=[
+            f"Price vs Kalman Level — {config.trading_pair}",
+            "Kalman Slope ($/candle)",
+            "Innovation ($)"
+        ],
+        row_heights=[0.5, 0.25, 0.25],
+        vertical_spacing=0.07,
+    )
+    fig.add_trace(go.Scatter(x=x_axis, y=prices.tolist(), name="Close",
+                             line=dict(color="#94a3b8", width=1)), row=1, col=1)
+    fig.add_trace(go.Scatter(x=x_axis, y=levels.tolist(), name="Kalman Level",
+                             line=dict(color="#60a5fa", width=2)), row=1, col=1)
+    slope_colors = ["#22c55e" if s > 0 else "#ef4444" for s in slopes]
+    fig.add_trace(go.Bar(x=x_axis, y=slopes.tolist(), name="Slope",
+                         marker_color=slope_colors, showlegend=False), row=2, col=1)
+    fig.add_hline(y=0, line_dash="dash", line_color="gray", line_width=1, row=2, col=1)
+    inn_colors = ["#f97316" if abs(i) > 2 * sigma_inn else "#64748b" for i in innovations]
+    fig.add_trace(go.Bar(x=x_axis, y=innovations.tolist(), name="Innovation",
+                         marker_color=inn_colors, showlegend=False), row=3, col=1)
+    fig.add_hline(y=sigma_inn,  line_dash="dot", line_color="#22c55e", line_width=1, row=3, col=1)
+    fig.add_hline(y=-sigma_inn, line_dash="dot", line_color="#22c55e", line_width=1, row=3, col=1)
+    fig.update_layout(
+        height=700, template="plotly_dark",
+        title=f"Kalman Regime: {regime} | SNR={snr:.3f} | {config.candle_interval} × {n}",
+        legend=dict(orientation="h", yanchor="top", y=-0.15, xanchor="center", x=0.5),
+    )
+
+    builder = ReportBuilder("Kalman Regime Check")
+    builder.source("routine", "kalman_regime_check")
+    builder.tags(["analysis", "regime", "kalman", "adaptive-grid-trader"])
+
+    builder.section("01 / REGIME SIGNAL", "Kalman filter output and profile recommendation")
+    builder.kpi("Regime", regime)
+    builder.kpi("Profile", profile)
+    builder.kpi("Current Price", f"${recent_price:,.2f}")
+    builder.kpi("Kalman Slope", f"{slope_pct:+.5f}%/candle")
+    builder.kpi("Innovation RMS", f"${sigma_inn:,.2f}")
+    builder.kpi("SNR", f"{snr:.3f}")
+    builder.kpi("Candles Used", str(n))
+
+    builder.section("02 / GRID SIZING", "Kalman-derived grid parameters")
+    builder.kpi("Grid D", f"${grid_D:,.2f}")
+    builder.kpi("Grid Start", f"${g_start:,.2f}")
+    builder.kpi("Grid End", f"${g_end:,.2f}")
+    builder.kpi("Limit Price", f"${g_limit:,.2f}" if g_limit else "N/A (ranging/uncertain)")
+
+    builder.section("03 / CHARTS", "Price, slope, and innovation over lookback window")
+    builder.plotly(fig)
+
+    builder.manual_order()
+    await builder.save()
+
+    return (
+        f"Regime: {regime} → {profile}\n"
+        f"Price ${recent_price:,.2f} | Slope {slope_pct:+.5f}%/candle | SNR {snr:.3f}\n"
+        f"Grid D ${grid_D:,.2f} | Range ${g_start:,.2f}–${g_end:,.2f}"
+        + (f" | Limit ${g_limit:,.2f}" if g_limit else "")
+    )

agents/adaptive_grid_trader/strategies/kalman_grid_btc/strategy.md

@@ -0,0 +1,106 @@
+---
+name: kalman_grid_btc
+description: ''
+agent_key: null
+skills: []
+default_config: {}
+default_trading_context: ''
+created_by: 456181693
+created_at: '2026-08-13T03:14:12.657657+00:00'
+---
+
+
+# Kalman Grid Strategy — BTC-USDT / bitget_perpetual
+
+## Envelope (fixed — never ask per tick)
+- connector: bitget_perpetual
+- pair: BTC-USDT
+- budget: 100 USDT | reserve: 10% | trade_budget: $90
+- max_leverage: 5x | max_loss_pct: 10% ($10 max per grid)
+- min_order_size: 5 USDT
+- allowed_profiles: LONG, SHORT only (no TWO_SIDED)
+
+## Every tick
+
+### 1 — Kalman signal
+```
+manage_routines(action="run", name="kalman_regime_check", config={
+  "connector_name": "bitget_perpetual",
+  "trading_pair": "BTC-USDT",
+  "candle_interval": "1m",
+  "lookback_candles": 60
+})
+```
+Extract from result: regime, profile, snr, grid_D, grid_start, grid_end, limit_price.
+
+### 2 — Executor state
+```
+manage_executors(action="list_executors")
+```
+Find any active grid_executor for BTC-USDT on bitget_perpetual. Note: executor_id, net_pnl_quote, filled_amount_quote, created_timestamp.
+
+### 3 — Decide (first match wins)
+
+**No executor running:**
+- profile LONG or SHORT → DEPLOY
+- else → HOLD
+
+**Executor running:**
+1. fills_amount_quote unchanged 10+ ticks → RETUNE (teardown + redeploy same direction)
+2. net_pnl_quote ≥ $2.00 → PROFIT_TAKE (teardown + redeploy if signal still directional)
+3. current grid_D differs >20% from deployed D → RETUNE (teardown + redeploy updated range)
+4. profile flipped AND executor age ≥ 180 min → FLIP (teardown + deploy new direction)
+5. else → KEEP
+
+### 4 — Act
+
+**DEPLOY grid:**
+```
+manage_executors(action="create_executor", executor_config={
+  "type": "grid_executor",
+  "connector_name": "bitget_perpetual",
+  "trading_pair": "BTC-USDT",
+  "start_price": <grid_start from Kalman>,
+  "end_price": <grid_end from Kalman>,
+  "limit_price": <limit from Kalman>,
+  "total_amount_quote": 90,
+  "n_levels": 9,
+  "min_spread_between_orders": 0.001,
+  "min_order_amount_quote": 5,
+  "leverage": 5,
+  "side": "BUY" if LONG else "SELL",
+  "time_limit": 86400,
+  "keep_position": false,
+  "triple_barrier_config": {
+    "take_profit": 0.003,
+    "stop_loss": 0.02,
+    "stop_loss_order_type": "MARKET"
+  }
+})
+```
+
+**STOP grid:**
+```
+manage_executors(action="stop_executor", executor_id=<id>)
+```
+Always verify flat (net position = 0) before redeploying.
+
+### 5 — Journal every tick
+```
+trading_agent_journal_write(entries=[
+  {"key": "regime",   "value": <regime>},
+  {"key": "snr",      "value": <snr>},
+  {"key": "grid_D",   "value": <grid_D>},
+  {"key": "profile",  "value": <profile>},
+  {"key": "action",   "value": <action taken>},
+  {"key": "pnl",      "value": <net_pnl_quote or 0>},
+  {"key": "fills",    "value": <filled_amount_quote or 0>}
+])
+```
+
+## Safety — abort and alert if:
+- Available balance < $90 before deploy
+- Worst-case loss at limit_price > $10
+- Liquidation price inside limit_price at 5x
+- Position cannot be verified flat after teardown
+