246 lines
9.1 KiB
Python
246 lines
9.1 KiB
Python
#!/usr/bin/env python3
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"""
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股票投研体系学习执行器
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======================
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根据当前阶段推进学习内容，输出本周学习任务和进度。
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用法:
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python3 stock_learning.py report → 输出本周学习报告
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python3 stock_learning.py advance → 进入下一阶段
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python3 stock_learning.py status → 查看当前状态
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"""
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import json, os, sys
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from datetime import datetime, timezone
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HOME = os.path.expanduser("~")
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HERMES = HOME + "/.hermes"
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TOPICS_FILE = HERMES + "/proactive_learning/topics.json"
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PROGRESS_FILE = HERMES + "/proactive_learning/stock_progress.json"
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os.makedirs(HERMES + "/proactive_learning", exist_ok=True)
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def load():
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if os.path.exists(TOPICS_FILE):
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data = json.load(open(TOPICS_FILE))
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for t in data.get("topics", []):
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if t["name"] == "股票投研体系":
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return t
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return None
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def load_progress():
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if os.path.exists(PROGRESS_FILE):
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return json.load(open(PROGRESS_FILE))
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return {"current_phase": 1, "current_item": 0, "learning_notes": [], "test_results": []}
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def save_progress(p):
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with open(PROGRESS_FILE, "w") as f:
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json.dump(p, f, ensure_ascii=False, indent=2)
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# ===================== 学习内容库 =====================
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PHASES = {
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1: {
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"title": "股票基础概念",
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"items": [
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("A股交易规则", [
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"A股实行T+1制度：当天买，次日才能卖",
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"涨跌幅限制：主板±10%，创业板/科创板±20%",
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"集合竞价：9:15-9:25申报，9:25-9:30不接受新申报",
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"连续竞价：9:30-11:30、13:00-15:00",
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"ST股（特别处理）涨跌±5%",
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"注册制新股：上市前5日无涨跌幅限制",
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]),
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("K线基础", [
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"阳线（红/实心）：收盘价>开盘价，代表上涨",
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"阴线（绿/空心）：收盘价<开盘价，代表下跌",
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"十字星：收盘≈开盘，多空力量均衡",
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"上影线：最高价到收盘/开盘的距离，上涨受阻",
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"下影线：最低价到收盘/开盘的距离，底部支撑",
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]),
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("核心指标", [
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"市盈率(PE)：股价/每股收益，越低越便宜但也可能是陷阱",
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"换手率：当日成交股数/总股本，高换手=活跃但高波动",
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"成交量：当日成交股数，量增价涨=健康上涨",
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"量比：当日每分钟平均成交量/历史平均，>1.5为放量",
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]),
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("均线系统", [
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"MA5：5日均线，短期趋势",
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"MA10：10日均线",
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"MA20：月线，中期趋势",
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"MA60：季线，重要支撑/压力位",
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"均线多头排列：短>长>短，价格强势",
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"均线空头排列：短<长<短，价格弱势",
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]),
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],
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},
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2: {
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"title": "技术分析框架",
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"items": [
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("MACD", [
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"MACD = 12日EMA - 26日EMA",
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"Signal线 = MACD的9日EMA",
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"金叉：MACD上穿Signal线，看涨信号",
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"死叉：MACD下穿Signal线，看跌信号",
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"柱状图：MACD-Signal线，红柱=多头，绿柱=空头",
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"顶背离：价格新高但MACD没新高，警惕回调",
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"底背离：价格新低但MACD没新低，警惕反弹",
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]),
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("KDJ", [
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"由RSV(未成熟随机值)计算得出",
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"K值：快速指标，对价格敏感",
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"D值：慢速指标，更稳定",
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"J值：3*K-2*D，波动最大",
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"KDJ金叉（K上穿D）+ 在20以下 = 超卖区买入信号",
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"KDJ死叉（K下穿D）+ 在80以上 = 超买区卖出信号",
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]),
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("成交量与价格", [
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"放量上涨：健康，看涨信号",
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"缩量上涨：可能见顶，动力不足",
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"放量下跌：恐慌抛售，可能见底",
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"缩量下跌：观望情绪，可能盘整",
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"地量见地价：极度缩量后可能反转",
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]),
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],
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},
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3: {
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"title": "基本面分析",
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"items": [
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("三张报表", [
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"利润表：展示收入、成本、利润",
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"资产负债表：展示资产、负债、净资产",
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"现金流量表：展示现金流入流出",
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"经营现金流>净利润：利润质量好",
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"商誉过高：警惕减值风险",
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]),
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("估值方法", [
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"PE（市盈率）：适合稳定增长的行业",
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"PB（市净率）：适合金融、周期行业",
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"PEG = PE/增长率，<1为低估",
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"DCF（现金流折现）：适合高确定性企业",
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]),
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],
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},
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4: {
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"title": "小唯量化工具链",
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"items": [
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("数据获取", [
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"akshare：免费，A股数据全面",
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"tushare：需要积分，接口更稳定",
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]),
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("技术指标计算", [
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"pandas/numpy处理行情数据",
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"TA-Lib计算MACD/KDJ/BOLL",
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"matplotlib可视化",
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]),
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],
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},
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5: {
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"title": "模拟交易与策略验证",
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"items": [],
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},
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}
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def load_industry_momentum():
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"""读取行业扫描结果（stock_industry_scan.py 生成）"""
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f = HERMES + "/stock_backtest/industry_scan.json"
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if not os.path.exists(f):
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return None
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try:
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return json.load(open(f))
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except Exception:
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return None
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def generate_report(progress, topic):
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phase = progress["current_phase"]
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item_idx = progress["current_item"]
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phase_data = PHASES.get(phase, {})
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report = f"**📈 股票投研体系学习报告**\n"
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report += f"时间: {datetime.now().strftime('%Y-%m-%d %H:%M')}\n"
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report += f"当前阶段: Phase {phase} - {phase_data.get('title', '完成')}\n\n"
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# 市场实时情报（2026-08-01 新增：行业动量 + 关注池机会）
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scan = load_industry_momentum()
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if scan and scan.get("industries"):
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report += "**🔭 市场行业动量（学术因子）**\n"
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try:
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mom_map = scan["industries"].get("avg_mom", {})
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ranked = sorted(mom_map.items(), key=lambda kv: kv[1], reverse=True)
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strong = [f"{ind}({mom:+.0%})" for ind, mom in ranked[:3] if mom > 0]
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weak = [f"{ind}({mom:+.0%})" for ind, mom in ranked[-3:]]
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if strong:
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report += f"🟢 强势行业: {' '.join(strong)}\n"
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if weak:
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report += f"🔴 弱势行业: {' '.join(weak)} (金叉硬拦截)\n"
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except Exception:
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pass
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report += "\n"
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if phase == 5:
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report += "**✅ 理论学习完成，开始模拟交易阶段**\n\n"
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report += "下一步：选择模拟平台（小唯推荐聚宽/掘金），搭建回测环境\n"
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return report
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items = phase_data.get("items", [])
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total_items = len(items)
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if item_idx < total_items:
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concept, points = items[item_idx]
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report += f"**本周主题：{concept}**\n"
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for i, p in enumerate(points, 1):
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report += f"{i}. {p}\n"
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report += f"\n📝 进度: {item_idx+1}/{total_items} 个概念\n"
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report += f"阶段进度: {item_idx*100//total_items}%\n"
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else:
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report += f"**阶段 {phase} 完成！**\n"
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report += f"\n✅ 已掌握：\n"
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for concept, points in items:
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report += f" - {concept}\n"
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report += f"\n下一阶段：Phase {phase+1} - {PHASES.get(phase+1, {}).get('title', '完成')}\n"
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report += f"\n回复「下一阶段」推进学习\n"
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# 历史学习记录
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if progress.get("learning_notes"):
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report += f"\n**📖 学习笔记** ({len(progress['learning_notes'])}条)\n"
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for note in progress["learning_notes"][-3:]:
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report += f" - {note[:60]}\n"
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return report
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if __name__ == "__main__":
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topic = load()
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progress = load_progress()
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cmd = sys.argv[1] if len(sys.argv) > 1 else "report"
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if cmd == "report":
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print(generate_report(progress, topic))
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elif cmd == "advance":
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# 推进到下一项或下一阶段
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phase = progress["current_phase"]
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phase_data = PHASES.get(phase, {})
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items = phase_data.get("items", [])
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item_idx = progress["current_item"]
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if item_idx + 1 < len(items):
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progress["current_item"] += 1
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elif phase + 1 in PHASES:
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progress["current_phase"] = phase + 1
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progress["current_item"] = 0
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else:
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print("全部阶段已完成！")
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sys.exit(0)
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save_progress(progress)
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print(generate_report(progress, topic))
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elif cmd == "status":
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print(json.dumps(progress, ensure_ascii=False, indent=2))
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else:
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print("用法: stock_learning.py [report|advance|status]") |