298 lines
11 KiB
Python
298 lines
11 KiB
Python
"""
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飞书复盘报告推送模块
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推送三类卡片：
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1. push_review_report — 策略复盘报告（蓝色主题）
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2. push_reverse_analysis_report — 逆向分析报告（紫色主题）
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3. push_rule_update_notification — 新规律通知（绿色主题）
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复用 feishu_push.py 的认证模式（load_feishu_creds → get_token → push_card）
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"""
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import os
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import sys
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import json
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sys.path.insert(0, os.path.dirname(__file__))
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from feishu_push import push_card
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CHAT_ID = "oc_2c597ad94167102922de142928e2917a"
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# ==================== 1. 策略复盘报告 ====================
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def push_review_report(review_results):
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"""
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推送策略复盘报告卡片 — 📊 蓝色主题
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Section 1: 推荐命中统计 (hit/fail/flat counts, hit rate %)
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Section 2: 信号绩效TOP5 (best performing signals)
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Section 3: 遗漏爆炸 (missed coins, why, what features)
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Section 4: 权重调整 (weight changes)
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"""
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reviews = review_results.get("review_details", [])
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weight_adj = review_results.get("weight_adjustments", [])
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missed = review_results.get("missed_explosions", [])
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# Section 1: 命中统计
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hit_count = sum(1 for r in reviews if r.get("outcome") == "爆发")
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fail_count = sum(1 for r in reviews if r.get("outcome") == "失败")
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flat_count = sum(1 for r in reviews if r.get("outcome") == "横盘")
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total = len(reviews)
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hit_rate_pct = round(hit_count / total * 100, 1) if total > 0 else 0
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# 命中统计文案
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hit_emoji = "🔥" if hit_rate_pct >= 50 else "⚠️" if hit_rate_pct >= 30 else "❌"
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stats_line = (
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f"本次复盘 **{total}** 条推荐:\n"
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f" • 爆发(命中): **{hit_count}** ({hit_emoji})\n"
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f" • 横盘: **{flat_count}**\n"
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f" • 失败: **{fail_count}**\n"
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f" • 命中率: **{hit_rate_pct}%**"
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)
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# Section 2: 信号绩效TOP5
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# 从review_results中提取信号绩效信息
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from altcoin_db import get_signal_weights
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weights = get_signal_weights()
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sig_perf_list = sorted(
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[(sig, data) for sig, data in weights.items() if data.get("total_count", 0) >= 3],
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key=lambda x: x[1].get("hit_rate", 0),
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reverse=True,
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)[:5]
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sig_lines = ""
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if sig_perf_list:
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for sig, data in sig_perf_list:
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hr = data.get("hit_rate", 0)
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w = data.get("weight", 0)
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total_n = data.get("total_count", 0)
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cat = data.get("category", "")
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emoji = "✅" if hr >= 50 else "⚠️" if hr >= 30 else "❌"
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sig_lines += f"\n • {emoji} **{sig}**({cat}): 命中率{hr}% | 权重{w} | 样本{total_n}"
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else:
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sig_lines = "\n • 样本不足，暂无绩效数据"
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# Section 3: 遗漏爆炸
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missed_lines = ""
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if missed:
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for m in missed[:5]: # 最多展示5只
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symbol = m.get("symbol", "")
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gain = m.get("gain_pct", 0)
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reason = m.get("reason_missed", m.get("reason", ""))
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features = m.get("features_detected", [])
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if isinstance(features, str):
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try:
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features = json.loads(features)
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except:
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features = [features]
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feat_str = ", ".join(str(f) for f in features[:3]) if features else "无"
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missed_lines += f"\n • 💥 **{symbol}** 涨{gain}% | 原因: {reason} | 特征: {feat_str}"
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else:
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missed_lines = "\n • ✅ 无遗漏爆炸"
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# Section 4: 权重调整
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adj_lines = ""
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if weight_adj:
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for adj in weight_adj:
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adj_lines += f"\n • {adj}"
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else:
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adj_lines = "\n • 无权重调整"
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# 构建卡片
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card = {
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"config": {"wide_screen_mode": True},
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"header": {
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"template": "blue",
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"title": {"tag": "plain_text", "content": "📊 山寨币策略复盘报告"},
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},
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"elements": [
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{
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"tag": "div",
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"text": {
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"tag": "lark_md",
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"content": (
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f"**=== 推荐命中统计 ===**\n{stats_line}\n\n"
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f"**=== 信号绩效TOP5 ===**\n{sig_lines}\n\n"
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f"**=== 遗漏爆炸 ===**\n{missed_lines}\n\n"
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f"**=== 权重调整 ===**\n{adj_lines}"
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),
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},
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},
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{
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"tag": "action",
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"actions": [
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{
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"tag": "button",
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"text": {"tag": "plain_text", "content": f"📊 命中率{hit_rate_pct}%"},
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"type": "primary" if hit_rate_pct >= 50 else "warning" if hit_rate_pct >= 30 else "danger",
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}
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],
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},
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],
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}
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return push_card(card)
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# ==================== 2. 逆向分析报告 ====================
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def push_reverse_analysis_report(reverse_results):
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"""
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推送逆向分析报告卡片 — 🔍 紫色主题
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Section 1: 今日涨幅榜TOP10 (symbol, gain%, sector)
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Section 2: 起爆前共性特征 (pattern summary with percentages)
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Section 3: 新发现规律 (any new rules)
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"""
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top_gainers = reverse_results.get("top_gainers", [])
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pattern_summary = reverse_results.get("pattern_summary", [])
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new_rules = reverse_results.get("new_rules", [])
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total_unrecommended = reverse_results.get("total_unrecommended", 0)
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total_analyzed = reverse_results.get("total_analyzed", 0)
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# Section 1: 涨幅榜TOP10
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gainer_lines = ""
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for i, g in enumerate(top_gainers[:10], 1):
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symbol = g.get("symbol", "").replace("/USDT", "")
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gain = g.get("gain_pct", 0)
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sector = g.get("sector", [])
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sector_str = sector[0] if isinstance(sector, list) and sector else (sector if sector else "未知")
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volume = g.get("volume_24h", 0)
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vol_str = f"${volume / 1e6:.1f}M" if volume > 0 else ""
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gainer_lines += f"\n {i}. **{symbol}** +{gain}% | {sector_str} | {vol_str}"
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if not gainer_lines:
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gainer_lines = "\n • 今日无明显涨幅"
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# Section 2: 起爆前共性特征
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pattern_lines = ""
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for p in pattern_summary[:8]: # 最多展示8个特征
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label = p.get("label", p.get("feature", ""))
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pct = p.get("percentage", 0)
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count = p.get("count", 0)
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total = p.get("total", 0)
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bar = "█" * int(pct / 10) + "░" * (10 - int(pct / 10))
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emoji = "🔥" if pct >= 60 else "✅" if pct >= 40 else "⚠️" if pct >= 20 else "❌"
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pattern_lines += f"\n • {emoji} **{label}**: {pct}%({count}/{total}) {bar}"
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if not pattern_lines:
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pattern_lines = "\n • 分析样本不足"
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# Section 3: 新发现规律
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rule_lines = ""
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if new_rules:
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for r in new_rules:
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rule_id = r.get("rule_id", "")
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desc = r.get("description", "")
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score_adj = r.get("score_adjust", 0)
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rule_type = r.get("type", "")
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rule_lines += f"\n • 🧠 **{rule_id}**: {desc} → 评分{score_adj}({rule_type})"
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else:
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rule_lines = "\n • 暂无新规律达到显著性阈值"
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# 分析概况
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overview = (
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f"涨幅榜共{len(top_gainers)}只 ≥10%\n"
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f"未被推荐: {total_unrecommended}只\n"
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f"已做PA分析: {total_analyzed}只"
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)
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# 构建卡片（紫色主题用 "violet" — 飞书卡片没有purple，用indigo近似）
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card = {
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"config": {"wide_screen_mode": True},
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"header": {
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"template": "indigo",
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"title": {"tag": "plain_text", "content": "🔍 逆向分析报告 — 涨幅榜复盘"},
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},
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"elements": [
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{
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"tag": "div",
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"text": {
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"tag": "lark_md",
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"content": (
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f"**{overview}**\n\n"
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f"**=== 今日涨幅榜TOP10 ===**\n{gainer_lines}\n\n"
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f"**=== 起爆前共性特征 ===**\n{pattern_lines}\n\n"
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f"**=== 新发现规律 ===**\n{rule_lines}"
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),
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},
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},
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{
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"tag": "action",
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"actions": [
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{
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"tag": "button",
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"text": {"tag": "plain_text", "content": f"🔍 分析{total_analyzed}只暴涨币"},
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"type": "primary",
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}
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],
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},
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],
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}
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return push_card(card)
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# ==================== 3. 新规律通知 ====================
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def push_rule_update_notification(rule_id, description, status="候选规则，未生效"):
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"""
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推送新规律学习通知 — 🧠 绿色主题
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简洁卡片，告知策略自动迭代
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"""
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card = {
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"config": {"wide_screen_mode": True},
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"header": {
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"template": "green",
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"title": {"tag": "plain_text", "content": "🧠 策略自学习 — 候选规则发现"},
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},
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"elements": [
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{
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"tag": "div",
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"text": {
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"tag": "lark_md",
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"content": (
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f"规则ID: **{rule_id}**\n\n"
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f"状态: **{status}**\n\n"
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f"描述: {description}\n\n"
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f"说明: 该规则仅进入候选池/灰度评估，未通过发布闸门前不会写入正式规则库，也不会影响下次选币。"
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),
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},
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},
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{
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"tag": "action",
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"actions": [
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{
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"tag": "button",
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"text": {"tag": "plain_text", "content": f"🧠 {rule_id}"},
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"type": "primary",
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}
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],
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},
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],
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}
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return push_card(card)
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# ==================== 测试 ====================
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if __name__ == "__main__":
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print("测试复盘报告推送...")
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# 测试review report
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test_review = {
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"review_details": [
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{"symbol": "FET/USDT", "outcome": "爆发", "pnl_48h": 12.5},
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{"symbol": "ARB/USDT", "outcome": "横盘", "pnl_48h": 1.2},
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{"symbol": "PEPE/USDT", "outcome": "失败", "pnl_48h": -4.5},
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],
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"weight_adjustments": ["量价齐飞: 3→4.0 (命中率67%)"],
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"missed_explosions": [
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{"symbol": "INJ/USDT", "gain_pct": 25, "reason_missed": "细筛淘汰(score=4)", "features_detected": ["ignition_point", "Q7_zone"]},
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],
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}
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ok1, r1 = push_review_report(test_review)
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print(f"复盘报告: ok={ok1}")
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# 测试rule notification
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ok2, r2 = push_rule_update_notification("rule_20260429_001", "涨幅榜60%有起爆点 → 起爆点是爆发前必现信号")
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print(f"规律通知: ok={ok2}") |