# source: https://raw.githubusercontent.com/shatianming5/Agent_market/389cd928020b2247909d47cecf41a78974d62755/create_workspace.py #!/usr/bin/env python3 """One-click workspace creation for OpenCode autonomous quant research. Usage: python create_workspace.py # creates ws_001/ python create_workspace.py --name my_research # creates ws_my_research/ python create_workspace.py --download-data # also downloads market data After creation, start OpenCode in the workspace: opencode run --dir ws_001 "Read GUIDE.md, then start autonomous research" """ from __future__ import annotations import argparse import json import shutil import sys from datetime import datetime from pathlib import Path ROOT = Path(__file__).resolve().parent def find_next_id() -> int: existing = sorted(ROOT.glob("ws_*")) max_id = 0 for d in existing: name = d.name if name.startswith("ws_") and name[3:].isdigit(): max_id = max(max_id, int(name[3:])) return max_id + 1 def create_workspace(name: str = "", download_data: bool = False) -> Path: if not name: ws_id = find_next_id() name = f"ws_{ws_id:03d}" ws = ROOT / name if ws.exists(): print(f"ERROR: {ws} already exists") sys.exit(1) print(f"Creating workspace: {ws}") # SOP directory structure for d in [ "strategies/type_A_trend", "strategies/type_B_meanrev", "strategies/type_C_pairs", "strategies/type_D_momentum", "strategies/type_E_hft", "strategies/type_F_ml", "strategies/type_G_event", "signals", "backtests/level1", "backtests/level2", "validation", "paper", "models", "configs", "results", "reports", "data", ]: (ws / d).mkdir(parents=True) # Copy core tools from workspace/ tools = [ "backtest_api.py", "evaluator.py", "tracker.py", "orchestrator.py", "model_loader.py", "auto_improver.py", "lookahead_checker.py", "walk_forward.py", "cost_model.py", "universe_selector.py", "feature_selector.py", "ensemble.py", "risk_manager.py", "paper_trader.py", "pairs_engine.py", "basket_engine.py", "deep_validate.py", "strategy_lifecycle.py", "performance_monitor.py", "continuous_runner.py", "gate_pipeline.py", "signal_validator.py", "adaptive_params.py", "report_generator.py", "research_report.py", "strategy_versioning.py", "visualizer.py", "cleanup.py", "sop.json", "objectives.json", ] src_ws = ROOT / "workspace" for tool in tools: src = src_ws / tool if src.exists(): shutil.copy2(src, ws / tool) # __init__.py (ws / "__init__.py").write_text(f"# Workspace {name}\n") # Symlink data (shared across workspaces) data_src = ROOT / "user_data" / "data" if data_src.exists(): data_link = ws / "data" if data_link.exists(): shutil.rmtree(data_link) data_link.symlink_to(data_src.resolve()) # Copy freqtrade configs for cfg in (src_ws / "configs").glob("*.json"): shutil.copy2(cfg, ws / "configs" / cfg.name) # Also copy from main configs for cfg in (ROOT / "user_data").glob("config_freqai*.json"): shutil.copy2(cfg, ws / "configs" / cfg.name) # Copy strategy templates for type_dir in (src_ws / "strategies").iterdir(): if type_dir.is_dir() and type_dir.name.startswith("type_"): dest = ws / "strategies" / type_dir.name dest.mkdir(parents=True, exist_ok=True) for py_file in type_dir.glob("*.py"): shutil.copy2(py_file, dest / py_file.name) # Copy freqtrade templates reference ft_templates = src_ws / "FREQTRADE_TEMPLATES.md" if ft_templates.exists(): shutil.copy2(ft_templates, ws / "FREQTRADE_TEMPLATES.md") # Copy agent loop script loop_script = src_ws / "run_agent_loop.sh" if loop_script.exists(): shutil.copy2(loop_script, ws / "run_agent_loop.sh") (ws / "run_agent_loop.sh").chmod(0o755) # Write GUIDE.md _write_guide(ws) # Write example strategy _write_example_strategy(ws) # Write run_agent.sh _write_run_agent(ws) # Metadata meta = { "workspace": name, "created": datetime.now().isoformat(), "version": "1.0", "tools": tools, "data_path": str(data_src.resolve()), } (ws / "meta.json").write_text(json.dumps(meta, indent=2)) # Download data if requested if download_data: print("Downloading market data...") _download_data(ws) print(f"\nWorkspace ready: {ws}") print(f"Start OpenCode agent:") print(f" cd {ws}") print(f" opencode run 'Read GUIDE.md and start autonomous quant research'") return ws def _download_data(ws: Path): """Download data using the download script.""" import subprocess download_script = ROOT / "workspace" / "download_data.py" if download_script.exists(): subprocess.run( [sys.executable, str(download_script), "--exchange", "gate", "--days", "400", "--outdir", str(ROOT / "user_data" / "data")], cwd=str(ROOT), timeout=600, ) def _write_guide(ws: Path): # Copy GUIDE_v2.md as the workspace guide guide_v2 = ROOT / "workspace" / "GUIDE_v2.md" if guide_v2.exists(): (ws / "GUIDE.md").write_text(guide_v2.read_text(encoding="utf-8"), encoding="utf-8") return guide = f"""# Workspace Research Guide ## 你是什么 你是一个自主量化研究 agent。你在 `{ws.name}/` 工作，目标是找到能盈利的交易策略。 ## 快速开始 ```python import sys; sys.path.insert(0, ".."); sys.path.insert(0, "../src") # 1. 扫描配对（已证明最有效的方法） from {ws.name}.pairs_engine import PairsEngine, scan_pairs pairs = scan_pairs(exchange="gate", min_correlation=0.8) # 2. 回测最佳配对 pe = PairsEngine("LINK/USDT", "SOL/USDT", exchange="gate") signals = pe.generate_signals(lookback=80, entry_z=2.0, exit_z=0.5) bt = pe.backtest(signals, maker_fee_bps=1.0) print(f"Profit={{bt.profit_pct:+.2f}}%, Sharpe={{bt.sharpe:.2f}}") # 3. Walk-Forward 验证 from {ws.name}.walk_forward import WalkForwardValidator wf = WalkForwardValidator(train_bars=2000, test_bars=500, step_bars=500) report = wf.validate("strategies/my_strategy.py", exchange="gate", pair="BTC/USDT") print(report.summary()) ``` ## 可用工具 ### 策略回测 ```python from {ws.name}.backtest_api import run_backtest result = run_backtest("strategies/xxx.py", timerange="20260107-20260125") # result = {{"ok": True, "sharpe": ..., "profit_pct": ..., "trades": ...}} ``` ### 配对交易（推荐！已验证盈利） ```python from {ws.name}.pairs_engine import PairsEngine, scan_pairs # 扫描协整配对 pairs = scan_pairs(exchange="gate") # 返回所有高相关+协整的配对 # 回测单个配对 pe = PairsEngine("ADA/USDT", "AVAX/USDT", exchange="gate") signals = pe.generate_signals(lookback=80, entry_z=2.0, exit_z=0.5) bt = pe.backtest(signals, maker_fee_bps=1.0) # maker 费率 # Walk-Forward 验证 df = pe.load_data() n = len(df) window_size = n // 5 for i in range(4): pe_w = PairsEngine("ADA/USDT", "AVAX/USDT", exchange="gate") pe_w._df = df.iloc[(i+1)*window_size:(i+2)*window_size].reset_index(drop=True) sig = pe_w.generate_signals(lookback=80, entry_z=2.0, exit_z=0.5) bt = pe_w.backtest(sig, maker_fee_bps=1.0) print(f"Window {{i+1}}: {{bt.profit_pct:+.2f}}%") ``` ### 多目标评估 ```python from {ws.name}.evaluator import evaluate score = evaluate(result) # {{"total_score": 78, "grade": "B", "suggestions": [...]}} ``` ### Walk-Forward 验证（必须通过才算有效） ```python from {ws.name}.walk_forward import WalkForwardValidator wf = WalkForwardValidator(train_bars=2000, test_bars=500, step_bars=500) report = wf.validate("strategies/my.py", exchange="gate", pair="BTC/USDT") # report.passed = True/False # report.mean_sharpe, report.pct_profitable_windows ``` ### 前瞻检查（回测前必查） ```python from {ws.name}.lookahead_checker import check_lookahead, fix_lookahead_issues report = check_lookahead("strategies/my.py") if not report.ok: fix_lookahead_issues("strategies/my.py") # 自动修复 bfill 等 ``` ### 交易成本 ```python from {ws.name}.cost_model import CostModel cm = CostModel(exchange="gate") est = cm.estimate_total_cost(trade_size_usd=500) print(f"Maker 往返: {{est.round_trip_bps:.0f}} bps") # ~2-5 bps # Taker 往返约 28 bps — 尽量用 maker 挂单 ``` ### 风控 ```python from {ws.name}.risk_manager import RiskManager rm = RiskManager(max_drawdown_pct=5.0) decision = rm.check_trade(signal_strength=0.8, win_rate=0.55, ...) ``` ### 实验追踪 ```python from {ws.name}.tracker import record_experiment, query_best, compare record_experiment(backtest_result=bt, evaluation=ev, strategy_name="xxx") best = query_best("sharpe", 5) # 历史最佳 ``` ## 已验证的策略（baseline） | 策略 | 类型 | Mean Profit | Sharpe | 状态 | |------|------|-------------|--------|------| | LINK/SOL + ADA/AVAX 配对 | 市场中性 | +2.03% | +0.65 | **最佳** | | 5配对组合 | 市场中性 | +0.53% | +0.42 | 稳定 | | v4 RSI+BB+divergence | 方向性 | -0.06% | +9.30 | WF通过但不赚钱 | ## 数据 | 交易所 | 品种 | 时间框架 | 行数 | 天数 | |--------|------|----------|------|------| | Gate.io | BTC,ETH,SOL,DOGE,XRP,AVAX,ADA,DOT,LINK | 1h | 9601 | 400 | | Gate.io | BTC,ETH,SOL,DOGE,XRP,AVAX | 5m | 8353 | 29 | | KuCoin | BTC,ETH | 1h | 1448 | 60 | 下载更多: `python download_data.py --exchange gate --days 400` ## 写策略的规范 继承 `IStrategy`，放在 `strategies/` 目录下： ```python from freqtrade.strategy import IStrategy class Github_shatianming5_Agent_market__create_workspace__20260608_143645(IStrategy): timeframe = "1h" can_short = False minimal_roi = {{"0": 0.08}} stoploss = -0.04 def populate_indicators(self, dataframe, metadata): # 计算指标 return dataframe def populate_entry_trend(self, dataframe, metadata): # 设置 enter_long = 1 return dataframe def populate_exit_trend(self, dataframe, metadata): # 设置 exit_long = 1 return dataframe ``` ## 研究建议 1. **优先做配对交易** — 已验证盈利，市场中性不怕熊市 2. **用 maker 费率** — 1 bps vs taker 10 bps，差 5-8% 绝对收益 3. **必须 Walk-Forward** — 单次回测不可信 4. **必须前瞻检查** — bfill() 等会造成虚假盈利 5. **少交易 > 多交易** — 成本是最大的敌人 """ (ws / "GUIDE.md").write_text(guide, encoding="utf-8") def _write_example_strategy(ws: Path): code = '''"""Example: RSI+BB Mean Reversion — copy and modify.""" from __future__ import annotations import sys from pathlib import Path import numpy as np from pandas import DataFrame _ROOT = Path(__file__).resolve().parents[1] sys.path.insert(0, str(Path(_ROOT).parent / "src")) sys.path.insert(0, str(Path(_ROOT).parent)) from freqtrade.strategy import IStrategy class Github_shatianming5_Agent_market__create_workspace__20260608_143645(IStrategy): timeframe = "1h" minimal_roi = {"0": 0.08, "120": 0.03} stoploss = -0.04 trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.02 use_exit_signal = True startup_candle_count = 30 can_short = False def populate_indicators(self, df: DataFrame, metadata: dict) -> DataFrame: delta = df["close"].diff() gain = delta.where(delta > 0, 0.0).rolling(14).mean() loss = (-delta.where(delta < 0, 0.0)).rolling(14).mean() df["rsi"] = 100 - (100 / (1 + gain / (loss + 1e-10))) sma = df["close"].rolling(20).mean() std = df["close"].rolling(20).std() df["bb_lower"] = sma - 2.0 * std df["bb_middle"] = sma return df def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: df.loc[(df["close"] < df["bb_lower"]) & (df["rsi"] < 35), ["enter_long", "enter_tag"]] = (1, "oversold") return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: df.loc[(df["close"] > df["bb_middle"]) | (df["rsi"] > 65), "exit_long"] = 1 return df ''' (ws / "strategies" / "example_rsi_bb.py").write_text(code, encoding="utf-8") def _write_run_agent(ws: Path): script = f"""#!/bin/bash # Start OpenCode agent in this workspace cd "$(dirname "$0")" opencode run -m custom/gpt-5.2 \\ "Read GUIDE.md in this directory. You are an autonomous quant researcher. \\ Your goal: find profitable trading strategies using the tools documented in GUIDE.md. \\ Start by scanning cointegrated pairs, then backtest, then walk-forward validate. \\ Record all experiments. Iterate until you find a strategy with positive mean profit \\ across walk-forward windows." """ run_path = ws / "run_agent.sh" run_path.write_text(script, encoding="utf-8") run_path.chmod(0o755) def main(): parser = argparse.ArgumentParser(description="Create a new quant research workspace") parser.add_argument("--name", default="", help="Workspace name (default: auto-increment ws_NNN)") parser.add_argument("--download-data", action="store_true", help="Download market data") args = parser.parse_args() create_workspace(name=args.name, download_data=args.download_data) if __name__ == "__main__": main()