feat: multi-strategy parallel screening (balanced_alpha+dual_low+quality_value)
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+108
-109
@@ -1,35 +1,27 @@
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#!/usr/bin/env python3
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"""
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mo_alphasift_bridge.py — AlphaSift 选股 → MoFin 自选池自动对接
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mo_alphasift_bridge.py — AlphaSift 多策略并行选股 → MoFin 自选池
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流程：
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1. 提交 AlphaSift 异步选股任务 → 轮询结果
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2. 过滤（评分阈值 + 去重）
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3. 写入 MoFin watchlist.json（含来源/日期/策略备注）
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4. 调用 regenerate_all() 生成策略
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5. price_monitor 自动接管
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支持同时跑多个策略，合并去重后写入自选池。
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默认三策略: balanced_alpha + dual_low + quality_value
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用法：
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python3 mo_alphasift_bridge.py # 默认策略
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用法:
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python3 mo_alphasift_bridge.py # 默认三策略
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python3 mo_alphasift_bridge.py --strategy dual_low # 单策略
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python3 mo_alphasift_bridge.py --dry-run # 只看不写
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python3 mo_alphasift_bridge.py --min-score 6 # 最低评分
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python3 mo_alphasift_bridge.py --strategy list # 列出所有策略
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cron: 0 10 * * 1-5 cd /home/hmo/MoFin && python3 mo_alphasift_bridge.py
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"""
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import sys, os, json, argparse, urllib.request, time
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from datetime import datetime
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from pathlib import Path
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# ── 配置 ─────────────────────────────────────────────────────────────
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DSA_API = "http://127.0.0.1:8001"
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MOFIN_DATA = Path("/home/hmo/web-dashboard/data")
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WATCHLIST_PATH = MOFIN_DATA / "watchlist.json"
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PORTFOLIO_PATH = MOFIN_DATA / "portfolio.json"
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DEFAULT_STRATEGY = "balanced_alpha"
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DEFAULT_STRATEGIES = "balanced_alpha,dual_low,quality_value"
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DEFAULT_MARKET = "cn"
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DEFAULT_MAX = 15
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MIN_SCORE = 5
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@@ -39,17 +31,13 @@ POLL_TIMEOUT = 300
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def load_json(path):
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try:
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return json.loads(path.read_text(encoding="utf-8"))
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except:
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return None
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try: return json.loads(path.read_text(encoding="utf-8"))
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except: return None
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def save_json(path, data):
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path.parent.mkdir(parents=True, exist_ok=True)
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path.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
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def api(endpoint, method="GET", body=None):
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url = f"{DSA_API}{endpoint}"
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data = json.dumps(body).encode() if body else None
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@@ -59,99 +47,119 @@ def api(endpoint, method="GET", body=None):
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with urllib.request.urlopen(req, timeout=30) as r:
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return json.loads(r.read())
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except Exception as e:
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print(f" API 错误: {e}")
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print(f" API错误: {e}")
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return None
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def get_existing_codes():
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codes = set()
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for path in [WATCHLIST_PATH, PORTFOLIO_PATH]:
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data = load_json(path)
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if not data:
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continue
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if not data: continue
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key = "stocks" if "watchlist" in str(path) else "holdings"
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for item in data.get(key, []):
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c = str(item.get("code", "")).strip()
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if c:
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codes.add(c)
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if c: codes.add(c)
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return codes
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def run_screen(strategy, market, max_results, dry_run=False):
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now = datetime.now()
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date_str = now.strftime("%Y-%m-%d")
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time_str = now.strftime("%Y-%m-%d %H:%M")
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def run_one_strategy(strategy, market, max_results):
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"""跑单个策略，返回候选股列表"""
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print(f"\n{'='*50}")
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print(f"策略: {strategy}")
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print(f"{'='*50}")
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# 1. 提交异步任务
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print(f"AlphaSift 选股: {strategy} (异步)", flush=True)
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task = api("/api/v1/alphasift/screen/tasks", "POST", {
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"strategy": strategy, "market": market, "max_results": max_results
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})
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if not task or not task.get("task_id"):
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print(" FAIL: 提交任务失败")
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return
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print(f" FAIL: 提交失败")
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return []
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task_id = task["task_id"]
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print(f" 任务ID: {task_id[:12]}...", flush=True)
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print(f" 任务: {task_id[:12]}...", flush=True)
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# 2. 轮询
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waited = 0
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result = None
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while waited < POLL_TIMEOUT:
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time.sleep(POLL_INTERVAL)
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waited += POLL_INTERVAL
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status = api(f"/api/v1/alphasift/screen/tasks/{task_id}")
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if not status:
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continue
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if not status: continue
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s = status.get("status", "")
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if s == "completed":
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print(f" 完成 ({waited}s)")
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result = status.get("result", {})
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break
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elif s == "failed":
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print(f" FAIL: {status.get('error', '')}")
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return
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else:
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pct = status.get("progress", 0)
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print(f" ...{s} ({pct}%)", flush=True)
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else:
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print(f" FAIL: 超时 ({POLL_TIMEOUT}s)")
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return
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candidates = result.get("candidates", [])
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if not candidates:
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print(" 无候选股")
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return
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print(f" 候选: {len(candidates)} 只")
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if candidates:
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for c in candidates[:3]:
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print(f" {c.get('code','?')} {c.get('name',c.get('title','?'))} 评分{c.get('score','?'):.1f}")
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return candidates
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elif s == "failed":
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print(f" FAIL: {status.get('error','')}")
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return []
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else:
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if waited % 60 == 0:
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print(f" ...{s} ({status.get('progress',0)}%)", flush=True)
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market_view = result.get("llm_market_view", "")
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print(f" 返回 {len(candidates)} 只候选股")
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if market_view:
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print(f" 市场观点: {market_view[:100]}...")
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print(f" FAIL: 超时")
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return []
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# 3. 过滤 + 去重
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existing = get_existing_codes()
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new_stocks = []
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skipped = 0
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def run_all(strategies_str, market, max_results, dry_run=False):
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"""多策略并行 → 合并去重 → MoFin 自选池"""
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strategies = [s.strip() for s in strategies_str.split(",") if s.strip()]
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now = datetime.now()
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date_str = now.strftime("%Y-%m-%d")
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time_str = now.strftime("%Y-%m-%d %H:%M")
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print(f"AlphaSift 多策略选股: {', '.join(strategies)}")
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print(f"开始: {time_str}")
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# 逐个跑策略，汇总
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all_candidates = []
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seen = set()
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for strategy in strategies:
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candidates = run_one_strategy(strategy, market, max_results)
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for c in candidates:
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code = str(c.get("code", "")).strip()
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if code in seen: continue
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seen.add(code)
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c["_strategy"] = strategy
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all_candidates.append(c)
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if not all_candidates:
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print("\n无候选股")
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return
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print(f"\n汇总: {len(all_candidates)} 只候选股 (去重后)")
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for s in strategies:
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cnt = sum(1 for c in all_candidates if c.get("_strategy") == s)
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print(f" {s}: {cnt} 只")
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# 过滤
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existing = get_existing_codes()
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new_stocks = []
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skipped_score = 0
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skipped_dup = 0
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for c in all_candidates:
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code = str(c.get("code", "")).strip()
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score = c.get("score", 0) or c.get("llm_score", 0) or 0
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if score < MIN_SCORE:
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skipped_score += 1; continue
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if code in existing:
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skipped_dup += 1; continue
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name = c.get("name", "") or c.get("title", "") or code
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reason = c.get("reason", "") or c.get("llm_thesis", "")
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catalysts = c.get("llm_catalysts", "")
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risks = c.get("llm_risks", "")
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src = c.get("_strategy", "unknown")
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if score < MIN_SCORE:
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skipped += 1
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continue
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if code in existing:
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continue
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factors = c.get("factor_scores", {})
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factor_note = ", ".join(f"{k}={v:.0f}" for k,v in list(factors.items())[:3]) if factors else ""
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notes_parts = [f"AlphaSift/{strategy} 评分{score}"]
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if reason:
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notes_parts.append(reason[:150])
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if catalysts:
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notes_parts.append(f"催化剂:{catalysts[:100]}")
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notes = f"AlphaSift/{src} 评分{score:.0f}"
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if factor_note: notes += f" [{factor_note}]"
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if reason: notes += f" | {reason[:120]}"
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new_stocks.append({
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"code": code,
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@@ -159,83 +167,74 @@ def run_screen(strategy, market, max_results, dry_run=False):
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"price": c.get("price", 0),
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"source": "alpha_sift",
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"source_detail": {
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"strategy": strategy,
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"strategy": src,
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"strategies_run": strategies,
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"score": score,
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"factor_scores": factors,
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"date": date_str,
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"reason": reason[:300],
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"catalysts": catalysts[:200] if catalysts else "",
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"risks": risks[:200] if risks else "",
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"market_view": market_view[:300] if market_view else "",
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},
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"notes": " | ".join(notes_parts),
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"notes": notes,
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"added_at": time_str,
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"added_by": "AlphaSift",
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"analysis": {},
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})
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existing.add(code)
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print(f" 过滤: {len(new_stocks)} 新标的 (跳过{skipped}只评分不足)")
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print(f"\n过滤: {len(new_stocks)} 新标的 (评分不足{skipped_score} + 重复{skipped_dup})")
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if not new_stocks:
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print(" 无符合条件的新标的")
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print("无符合条件的新标的")
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return
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new_stocks = new_stocks[:MAX_ADD]
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print(f"\n 新增 {len(new_stocks)} 只到自选池:")
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print(f"\n新增 {len(new_stocks)} 只到自选池:")
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for s in new_stocks:
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print(f" {s['code']} {s['name']} (评分{s['source_detail']['score']})")
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if s['source_detail'].get('reason'):
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print(f" {s['source_detail']['reason'][:120]}")
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sd = s["source_detail"]
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print(f" {s['code']} {s['name']} ({sd['strategy']} 评分{sd['score']:.0f})")
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if dry_run:
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print("\n [DRY RUN] 未写入")
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print("\n[DRY RUN] 未写入")
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return
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# 4. 写入
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# 写入
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wl = load_json(WATCHLIST_PATH) or {"stocks": []}
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wl["stocks"].extend(new_stocks)
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wl["updated_at"] = time_str
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save_json(WATCHLIST_PATH, wl)
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print(f"\n 已写入 {WATCHLIST_PATH}")
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print(f"\n已写入 {WATCHLIST_PATH}")
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# 5. 策略生成
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print("\n 调用 regenerate_all() 生成策略...")
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# 策略生成
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print("\n调用 regenerate_all()...")
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try:
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sys.path.insert(0, str(MOFIN_DATA.parent))
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from strategy_lifecycle import regenerate_all
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r = regenerate_all(stdout=True)
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if r:
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print(f" 完成: {r.get('ok',0)}/{r.get('total',0)} 只策略已生成")
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print(f" price_monitor 将在 2 分钟内自动开始监控")
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if r: print(f"完成: {r.get('ok',0)}/{r.get('total',0)} 只策略已生成")
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except Exception as e:
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print(f" WARN: regenerate_all 失败: {e}")
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print(f" 股票已加入自选池，下次 cron 会自动处理")
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print(f"WARN: {e}")
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def list_strategies():
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r = api("/api/v1/alphasift/strategies")
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if r and r.get("strategies"):
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print("AlphaSift 策略:")
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for s in r["strategies"]:
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print(f" {s['id']:20s} {s.get('name','?'):15s} {s.get('description','')[:60]}")
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print(f" {s['id']:22s} {s.get('name','?'):10s} {s.get('description','')[:60]}")
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def main():
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global MIN_SCORE
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parser = argparse.ArgumentParser(description="AlphaSift → MoFin")
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parser.add_argument("--strategy", default=DEFAULT_STRATEGY)
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parser.add_argument("--market", default=DEFAULT_MARKET)
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parser.add_argument("--max", type=int, default=DEFAULT_MAX)
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parser.add_argument("--min-score", type=int, default=MIN_SCORE)
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parser.add_argument("--dry-run", action="store_true")
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args = parser.parse_args()
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p = argparse.ArgumentParser(description="AlphaSift → MoFin")
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p.add_argument("--strategy", default=DEFAULT_STRATEGIES)
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p.add_argument("--market", default=DEFAULT_MARKET)
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p.add_argument("--max", type=int, default=DEFAULT_MAX)
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p.add_argument("--min-score", type=int, default=MIN_SCORE)
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p.add_argument("--dry-run", action="store_true")
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args = p.parse_args()
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MIN_SCORE = args.min_score
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if args.strategy == "list":
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list_strategies()
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return
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run_screen(args.strategy, args.market, args.max, args.dry_run)
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if args.strategy == "list": list_strategies()
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else: run_all(args.strategy, args.market, args.max, args.dry_run)
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if __name__ == "__main__":
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