Fix/fee bleed and cooldown

README.md

@@ -137,7 +137,7 @@ Market Data -> Composite Fair Value -> Dynamic Spread -> Inventory Skew -> Multi
 | Provider | Models | Env Variable |
 |----------|--------|-------------|
 | Google Gemini | `gemini-2.0-flash` (default), `gemini-2.5-pro` | `GEMINI_API_KEY` |
-| Anthropic Claude | `claude-haiku-4-5-20251001`, `claude-sonnet-4-20250514` | `ANTHROPIC_API_KEY` |
+| Anthropic Claude | `claude-haiku-4-5-20251001`, `claude-sonnet-4-20250514` | `ANTHROPIC_API_KEY` or `ANTHROPIC_SESSION_TOKEN` |
 | OpenAI | `gpt-4o`, `gpt-4o-mini`, `o3-mini` | `OPENAI_API_KEY` |
 
 ---
@@ -648,7 +648,8 @@ hl run my_strategies.my_strategy:MyStrategy -i ETH-PERP --tick 10
 | `HL_TESTNET` | No | `true` (default) or `false` for mainnet |
 | `BUILDER_ADDRESS` | No | Override builder fee address |
 | `BUILDER_FEE_TENTHS_BPS` | No | Override fee rate (default: 100 = 10 bps) |
-| `ANTHROPIC_API_KEY` | No | For `claude_agent` with Claude |
+| `ANTHROPIC_API_KEY` | No | For `claude_agent` with Claude (API key) |
+| `ANTHROPIC_SESSION_TOKEN` | No | For `claude_agent` with Claude (Claude Max session token) |
 | `GEMINI_API_KEY` | No | For `claude_agent` with Gemini |
 | `OPENAI_API_KEY` | No | For `claude_agent` with OpenAI |
 

cli/commands/mcp.py

@@ -13,6 +13,8 @@
 def mcp_serve(
     transport: str = typer.Option("stdio", "--transport", "-t",
                                    help="Transport mode: stdio or sse"),
+    port: int = typer.Option(18790, "--port", "-p",
+                             help="Port for SSE transport (ignored for stdio)"),
 ):
     """Start MCP server exposing trading tools for AI agents."""
     project_root = str(Path(__file__).resolve().parent.parent.parent)

cli/commands/setup.py

@@ -59,10 +59,10 @@ def setup_check():
         ok_items.append("Builder fee: not configured (optional)")
 
     # 5. LLM key (for claude_agent)
-    if os.environ.get("ANTHROPIC_API_KEY") or os.environ.get("GEMINI_API_KEY"):
-        ok_items.append("LLM API key found")
+    if os.environ.get("ANTHROPIC_API_KEY") or os.environ.get("ANTHROPIC_SESSION_TOKEN") or os.environ.get("GEMINI_API_KEY"):
+        ok_items.append("LLM API key/session token found")
     else:
-        ok_items.append("LLM API key: not set (only needed for claude_agent strategy)")
+        ok_items.append("LLM API key/session token: not set (only needed for claude_agent strategy)")
 
     # 6. Data directories
     data_dir = Path("data/cli")

cli/hl_adapter.py

@@ -168,8 +168,8 @@ def get_account_state(self) -> Dict:
             log.error("Failed to get account state: %s", e)
             return {}
 
-        # Merge HIP-3 DEX positions (e.g. YEX) so watchdog/reconciliation sees them.
-        for dex_id in HIP3_DEXS:
+        # Merge HIP-3 DEX positions (e.g. YEX) — testnet only, not available on mainnet.
+        for dex_id in (HIP3_DEXS if self._hl.testnet else {}):
             try:
                 dex_state = self._info.post("/info", {
                     "type": "clearinghouseState", "user": self._address, "dex": dex_id,
@@ -324,18 +324,25 @@ def place_order(
         # Round price to HL tick size (price-dependent, 5 sig figs)
         price = self._round_price(price, coin)
 
-        # For IOC orders, apply slippage to cross the spread and guarantee fill.
-        # Strategy prices are often at fair value (inside the spread) which won't
-        # match any resting orders. Push buys above ask, sells below bid.
-        if tif == "Ioc":
-            try:
-                snap = self._hl.get_snapshot(instrument)
+        # Price adjustment based on order type:
+        # - IOC: push past spread to guarantee fill (taker)
+        # - ALO: sit on maker side of book for rebates
+        try:
+            snap = self._hl.get_snapshot(instrument)
+            if tif == "Ioc":
                 if is_buy and snap.ask > 0:
-                    price = max(price, self._round_price(snap.ask * SLIPPAGE_FACTOR, coin))
+                    price = max(price, self._round_price(
+                        snap.ask * SLIPPAGE_FACTOR, coin))
                 elif not is_buy and snap.bid > 0:
-                    price = min(price, self._round_price(snap.bid * (2 - SLIPPAGE_FACTOR), coin))
-            except Exception:
-                pass  # use original price if snapshot fails
+                    price = min(price, self._round_price(
+                        snap.bid * (2 - SLIPPAGE_FACTOR), coin))
+            elif tif == "Alo":
+                if is_buy and snap.bid > 0:
+                    price = self._round_price(snap.bid, coin)
+                elif not is_buy and snap.ask > 0:
+                    price = self._round_price(snap.ask, coin)
+        except Exception:
+            pass  # use original price if snapshot fails
 
         fill = self._send_order(coin, instrument, side, is_buy, size, price, tif, builder)
 
@@ -508,6 +515,7 @@ def place_trigger_order(self, instrument: str, side: str, size: float, trigger_p
         coin = self._to_coin(instrument)
         is_buy = side.lower() == "buy"
         sz = self._round_size(coin, size)
+        trigger_price = self._round_price(trigger_price, coin)
         try:
             result = self._exchange.order(
                 coin, is_buy, sz, trigger_price,

cli/skill.md

@@ -94,7 +94,8 @@ hl apex run --mainnet                                   # APEX multi-slot
 | `HL_TESTNET` | No | `true` (default) or `false` for mainnet |
 | `BUILDER_ADDRESS` | No | Override builder fee address (default: hardcoded) |
 | `BUILDER_FEE_TENTHS_BPS` | No | Override fee rate (default: 100 = 10 bps) |
-| `ANTHROPIC_API_KEY` | No | For `claude_agent` strategy |
+| `ANTHROPIC_API_KEY` | No | For `claude_agent` strategy (API key) |
+| `ANTHROPIC_SESSION_TOKEN` | No | For `claude_agent` strategy (Claude Max session token) |
 | `GEMINI_API_KEY` | No | For `claude_agent` with Gemini |
 
 \* Either `HL_PRIVATE_KEY` or a keystore with `HL_KEYSTORE_PASSWORD` is required.

configs/apex_btc_mainnet.yaml

@@ -0,0 +1,17 @@
+# APEX config — BTC-PERP only, mainnet, $50 account
+# Single parameter focus: radar_score_threshold
+
+# Instrument filter
+allowed_instruments:
+  - BTC-PERP
+
+# Entry threshold — the single parameter to tune
+radar_score_threshold: 180
+
+# Scaled for $50 account
+total_budget: 50.0
+max_slots: 1
+leverage: 10.0
+daily_loss_limit: 10.0
+guard_preset: tight
+slot_cooldown_ms: 7200000  # 2 hours — prevents fee churn on re-entry

configs/autoresearch_program.md

@@ -1,7 +1,8 @@
 # Autoresearch Program: APEX Config Optimization
 
 ## Objective
-Optimize APEX trading strategy parameters by replaying historical trades through the backtest harness and maximizing net PnL while maintaining trade quality.
+Optimize the single entry threshold parameter for BTC-PERP mainnet trading by replaying
+historical trades through the backtest harness and maximizing net PnL.
 
 ## Mutable File
 `apex_config.json`
@@ -20,42 +21,33 @@ python3 scripts/backtest_apex.py --config apex_config.json --trades data/cli/tra
 - `trades` — should stay above 5 (quality gate)
 - `profit_factor` — should stay above 1.0
 
-## Parameter Bounds (guardrails)
+## Single Parameter: radar_score_threshold
 
-| Parameter                    | Min   | Max   | Step | Default |
-|------------------------------|-------|-------|------|---------|
-| radar_score_threshold        | 120   | 280   | 10   | 170     |
-| pulse_confidence_threshold   | 40.0  | 95.0  | 5.0  | 70.0    |
-| daily_loss_limit             | 50.0  | 5000.0| 50.0 | 500.0   |
-| max_same_direction           | 1     | 3     | 1    | 2       |
+| Parameter             | Min | Max | Step | Default |
+|-----------------------|-----|-----|------|---------|
+| radar_score_threshold | 120 | 280 | 10   | 170     |
 
-## Research Directions
-
-These are common exploration paths based on REFLECT findings:
-
-1. **High FDR (>30%)**: Raise `radar_score_threshold` in [170, 250] to filter low-quality entries that generate fees without sufficient edge.
-
-2. **Low Win Rate (<40%)**: Sweep `pulse_confidence_threshold` in [70, 95] to require higher conviction before entry.
+All other parameters are fixed. Do not modify them.
 
-3. **Direction Imbalance**: If one direction is consistently losing, set `max_same_direction` to 1 to force diversification.
-
-4. **Loss Streaks**: Reduce `daily_loss_limit` by 20% increments to cut drawdowns from consecutive losses.
-
-5. **Healthy Strategy**: If metrics look good (win_rate >50%, FDR <15%), try *lowering* `radar_score_threshold` in [140, 170] to capture more trades without degrading quality.
+## Research Directions
 
-6. **Fee Drag Emergency**: If fees exceed gross PnL, simultaneously raise `radar_score_threshold` to [220, 280] and `pulse_confidence_threshold` to [85, 95].
+1. **High FDR (>30%)**: Raise `radar_score_threshold` toward 250 — filter low-quality entries.
+2. **Low Win Rate (<40%)**: Raise `radar_score_threshold` toward 220 — require higher conviction.
+3. **Too few trades**: Lower `radar_score_threshold` toward 140 — loosen entry criteria.
+4. **Healthy metrics**: Try lowering `radar_score_threshold` toward 140 to capture more trades.
+5. **Fee Drag Emergency**: Raise `radar_score_threshold` to [220, 280].
 
 ## Workflow
 
-1. Start with the current `apex_config.json` as baseline
+1. Start with current `apex_config.json` as baseline
 2. Run backtest to get baseline metrics
-3. Pick a research direction based on the metrics
-4. Modify one parameter at a time within bounds
-5. Re-run backtest and compare
-6. If `REJECT: too few trades` appears, the config is too restrictive — back off
-7. Keep the config that maximizes `net_pnl` while passing all quality gates
+3. Pick a direction based on the metrics
+4. Change `radar_score_threshold` by one step (±10)
+5. Re-run backtest and compare `net_pnl`
+6. Keep if improved, revert if not
+7. Repeat until no improvement found
 
 ## Quality Gates
 - Must produce at least 5 round trips
-- `profit_factor` must be > 1.0 (net profitable)
-- `fdr` must be < 50% (fees not destroying all edge)
+- `profit_factor` must be > 1.0
+- `fdr` must be < 50%

deploy/openclaw-railway/Dockerfile

@@ -60,18 +60,18 @@ RUN mv /usr/bin/rg /usr/bin/rg-real \
 
 # Install agent-cli (our trading CLI)
 WORKDIR /agent-cli
-COPY ../../ .
+COPY . .
 RUN pip install --no-cache-dir --break-system-packages -e ".[mcp]"
 
 # Wrapper app
 WORKDIR /app
-COPY package.json ./
+COPY deploy/openclaw-railway/package.json ./
 RUN npm install --production
 
-COPY src ./src
+COPY deploy/openclaw-railway/src ./src
 
 # Workspace defaults (copied to volume at runtime)
-COPY workspace /opt/workspace-defaults
+COPY deploy/openclaw-railway/workspace /opt/workspace-defaults
 
 # Vendor mcporter skill
 RUN set -eux; \

deploy/openclaw-railway/src/bootstrap.mjs

@@ -23,6 +23,15 @@ const PROVIDER_MAP = {
   blockrun: { key: "blockrun-wallet-key", provider: "blockrun" },
 };
 
+function detectAuthMode(credentialValue, provider) {
+  // For Anthropic, check if it's an OAuth token (starts with sk-ant-oauth-)
+  // Otherwise assume API key authentication
+  if (provider === "anthropic" && credentialValue.startsWith("sk-ant-oauth-")) {
+    return "oauth";
+  }
+  return "token";
+}
+
 export async function bootstrap() {
   console.log("[bootstrap] Starting auto-configuration...");
 
@@ -48,7 +57,7 @@ export async function bootstrap() {
     }
   }
 
-  // 3. Generate openclaw.json with our MCP server
+  // 3. Generate openclaw.json with MCP server
   const config = buildConfig();
   writeFileSync(CONFIG_PATH, JSON.stringify(config, null, 2));
   console.log("[bootstrap] Generated openclaw.json");
@@ -84,49 +93,49 @@ function buildConfig() {
     ? (process.env.BLOCKRUN_WALLET_KEY || "")
     : aiKey;
 
-  const config = {
-    // Security (headless deployment)
-    deviceAuth: false,
-    insecureAuth: true,
-
-    // Agent settings
-    agentConcurrency: 10,
-    subagentConcurrency: 12,
-
-    // AI provider
-    provider: providerInfo.provider,
-    [providerInfo.key]: credentialValue,
+  const profileName = `${providerInfo.provider}:auto`;
+  const authMode = detectAuthMode(credentialValue, providerInfo.provider);
 
-    // MCP servers — our trading CLI is the primary tool provider
-    mcpServers: {
-      nunchi_trading: {
-        command: "python3",
-        args: ["-m", "cli.main", "mcp", "serve"],
-        cwd: "/agent-cli",
-        env: {
-          HL_PRIVATE_KEY: process.env.HL_PRIVATE_KEY || "",
-          HL_TESTNET: process.env.HL_TESTNET || "true",
-          ...(isBlockrun ? {
-            BLOCKRUN_WALLET_KEY: process.env.BLOCKRUN_WALLET_KEY || "",
-            BLOCKRUN_PROXY_PORT: process.env.BLOCKRUN_PROXY_PORT || "8402",
-          } : {}),
+  const config = {
+    gateway: {
+      mode: "local",
+    },
+    agents: {
+      defaults: {
+        maxConcurrent: 10,
+        subagents: { maxConcurrent: 12 },
+      },
+    },
+    auth: {
+      profiles: {
+        [profileName]: {
+          provider: providerInfo.provider,
+          mode: authMode,
+        },
+      },
+    },
+    tools: {
+      mcp: {
+        servers: {
+          "nunchi-trading": {
+            command: "python3",
+            args: ["-m", "cli.main", "mcp", "serve", "--transport", "stdio"],
+            cwd: "/agent-cli",
+            env: {
+              ...process.env,
+              PYTHONPATH: "/agent-cli",
+            },
+          },
         },
       },
     },
-
-    // Workspace
-    workspaceDir: WORKSPACE_DIR,
-    stateDir: STATE_DIR,
   };
 
   // Telegram integration
   if (process.env.TELEGRAM_BOT_TOKEN) {
     config.channels = {
       telegram: {
         botToken: process.env.TELEGRAM_BOT_TOKEN,
-        allowedUsers: process.env.TELEGRAM_USERNAME
-          ? [process.env.TELEGRAM_USERNAME.replace("@", "")]
-          : [],
       },
     };
   }