BankrBot · darks0l · Mar 21, 2026
diff --git a/autoresearch/SKILL.md b/autoresearch/SKILL.md
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+---
+name: autoresearch
+description: >
+  Karpathy-style autonomous trading strategy discovery for Base DEX.
+  AI agent iteratively mutates, backtests, and evolves strategies against
+  real Uniswap V3 + Aerodrome data on Base. Uses Bankr LLM Gateway for
+  strategy mutations and Bankr wallet for live execution. LCM memory
+  ensures each experiment builds on prior discoveries.
+  Triggers on: "discover trading strategy", "run autoresearch", "backtest strategy",
+  "optimize trading", "find alpha", "evolve strategy", "autonomous trading research",
+  "DEX strategy", "Base trading", "strategy mutation".
+---
+
+# AutoResearch — Autonomous Trading Strategy Discovery
+
+> Karpathy-style autoresearch for Base DEX. Iteratively mutates, backtests, and evolves
+> trading strategies using LLM-driven mutations and persistent experiment memory.
+
+**Repo:** https://github.com/darks0l/autoresearch
+**Pairs:** ETH/USDC, cbETH/WETH, AERO/USDC (Uniswap V3 + Aerodrome on Base)
+**Tests:** 38 passing | **Experiments:** 117+ logged | **Best Score:** 2.838 Sharpe
+
+---
+
+## How It Works
+
+1. **Read** current strategy + experiment history
+2. **Mutate** via Bankr LLM Gateway (claude-sonnet-4.5, or any of 27+ models)
+3. **Backtest** against real Base DEX data (CoinGecko hourly OHLCV)
+4. **Keep or revert** — only improvements survive
+5. **Log to memory** — every experiment indexed for future reference
+6. **Repeat** — each cycle gets smarter from accumulated knowledge
+
+One file changes (`strategies/strategy.js`). Everything else stays locked.
+
+---
+
+## Quick Start
+
+```bash
+# Clone the skill
+git clone https://github.com/darks0l/autoresearch.git
+cd autoresearch
+
+# Install (zero external deps beyond Node.js)
+npm install
+
+# Run autonomous research (30 experiments)
+node scripts/run-autoresearch.js --max 30
+
+# Or run the persistent daemon
+node scripts/daemon.js --batch 15 --model claude-sonnet-4.5
+```
+
+### Environment Variables
+
+| Variable | Description | Required |
+|----------|-------------|----------|
+| `BANKR_API_KEY` | Bankr API key for LLM mutations + wallet | Yes (for LLM) |
+| `BANKR_WALLET` | Bankr wallet address for live execution | No |
+| `UNISWAP_API_KEY` | Uniswap Developer Platform API key | No |
+| `BASE_RPC_URL` | Base RPC endpoint | No (defaults to mainnet.base.org) |
+| `AUTORESEARCH_MODEL` | LLM model for mutations | No (defaults to claude-sonnet-4.5) |
+
+---
+
+## Bankr Integration (3 Layers)
+
+### 1. LLM Gateway — Strategy Mutations
+The agent calls Bankr LLM Gateway (`llm.bankr.bot`) to generate strategy mutations.
+Each call includes the current strategy code, experiment history, and performance data.
+The LLM proposes one atomic change with a testable hypothesis.
+
+```javascript
+// POST https://llm.bankr.bot/v1/chat/completions
+{
+  "model": "claude-sonnet-4.5",
+  "messages": [
+    { "role": "system", "content": "You are a quantitative trading researcher..." },
+    { "role": "user", "content": "Current strategy:\n```js\n...\n```\nHistory: 117 experiments, best score 2.838..." }
+  ]
+}
+```
+
+### 2. Wallet Execution — Live Trading
+Optional live execution via Bankr wallet on Base. The executor module handles:
+- Position sizing with risk limits (15% max position, 5% daily loss limit)
+- Paper trading mode for validation before going live
+- Real swap execution via `POST /agent/prompt`
+
+### 3. Balance Tracking — Portfolio Sync
+Reads Bankr wallet state via `GET /agent/balances` for position tracking
+and credit monitoring via `GET /v1/credits`.
+
+---
+
+## Strategy Interface
+
+Your strategy must implement a `Strategy` class with an `onBar()` method:
+
+```javascript
+import { rsi, ema, bollingerBands, atr, vwap, roc } from '../src/indicators.js';
+
+export class Strategy {
+  constructor() {
+    // Initialize parameters and state
+  }
+
+  onBar(barData, portfolio) {
+    // barData: { 'ETH/USDC': { open, high, low, close, volume, history: [...bars] } }
+    // portfolio: { cash: number, positions: { pair: signedUsdNotional } }
+    // Return: [{ pair, targetPosition, orderType? }]
+    return [];
+  }
+}
+```
+
+---
+
+## Available Indicators
+
+All from `src/indicators.js` — pure math, no dependencies:
+
+- `sma(values, period)` — Simple Moving Average
+- `ema(values, period)` — Exponential Moving Average
+- `rsi(closes, period)` — Relative Strength Index (default 14)
+- `macd(closes, fast, slow, signal)` — MACD (default 12/26/9)
+- `bollingerBands(closes, period, stdDev)` — Bollinger Bands (default 20/2)
+- `atr(highs, lows, closes, period)` — Average True Range (default 14)
+- `vwap(closes, volumes, period)` — VWAP (default 20)
+- `roc(values, period)` — Rate of Change (default 10)
+- `stddev(values, period)` — Standard Deviation (default 20)
+- `percentileRank(values, period)` — Percentile Rank (default 100)
+
+---
+
+## Scoring System
+
+```
+score = sharpe × √(min(trades/50, 1.0)) − drawdown_penalty − turnover_penalty
+```
+
+- **Drawdown penalty:** `max(0, max_drawdown_pct − 15) × 0.05`
+- **Turnover penalty:** `max(0, annual_turnover/capital − 500) × 0.001`
+- **Hard cutoffs (→ −999):** Fewer than 10 trades, drawdown > 50%, lost > 50%
+
+---
+
+## Data Pipeline
+
+Three-tier data sourcing with disk caching:
+1. **CoinGecko** — Free hourly OHLCV (90 days, 703 bars per pair)
+2. **DeFiLlama** — Price data fallback
+3. **Synthetic GBM** — Last resort for testing (geometric Brownian motion)
+
+Cache TTL: 7 days. Pairs: ETH/USDC, ETH/USDC-30 (0.3% fee tier), cbETH/WETH, AERO/USDC.
+
+---
+
+## Modules
+
+| Module | Description |
+|--------|-------------|
+| `src/indicators.js` | 10 technical indicators, pure math |
+| `src/backtest.js` | Replay engine with fee model and scoring |
+| `src/controller.js` | Autoresearch loop orchestrator |
+| `src/memory.js` | LCM experiment memory layer |
+| `src/data.js` | OHLCV data fetcher with caching |
+| `src/datafeed.js` | Production data feed (DeFiLlama + CoinGecko) |
+| `src/regime.js` | Regime detection (Hurst, trend, volatility) |
+| `src/executor.js` | Production execution via Bankr wallet |
+| `src/bankr.js` | Bankr LLM Gateway + wallet integration |
+| `src/reporter.js` | Batch reporting for Discord/Telegram |
+| `src/config.js` | Configuration management |
+| `strategies/strategy.js` | THE mutable file (agent modifies only this) |
+
+---
+
+## OpenClaw Integration
+
+### As a Skill
+Drop into your OpenClaw skills directory (`~/.openclaw/skills/autoresearch/`).
+
+### As a Cron Job
+```
+"Run 15 autoresearch experiments, report results to #trading"
+```
+
+### Sub-Agent Pattern
+```javascript
+sessions_spawn({
+  task: "Run 50 autoresearch experiments. Report after each batch of 5.",
+  mode: "run"
+})
+```
+
+---
+
+## Key Discovery
+
+VWAP mean-reversion peaked at score 0.740 over 106 experiments on synthetic data.
+When switched to real CoinGecko data, it collapsed to **-1.46** — completely overfit.
+The system autonomously pivoted to adaptive trend-following (Donchian breakout +
+EMA filter + RSI dip-buying + ATR trailing stops) and achieved **score 2.838** on real data.
+
+The agent doesn't just execute trades — it discovers *how* to trade, and learns from its mistakes.
+
+---
+
+## Live Trade Proof
+
+- **TX:** [`0x752f7393...`](https://basescan.org/tx/0x752f73935fa93862fb37d14c09054785fdd983ce9bcc928af7ece91d3d69b4b8)
+- **Action:** 1 USDC → 0.000464 ETH on Base via Bankr wallet
+- **Chain:** Base mainnet
+
+---
+
+*Built by DARKSOL 🌑 — autonomous strategy discovery for Base DEX*