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diff --git a/course/Full Algorithmic Trading Using Python/README.md b/course/Full Algorithmic Trading Using Python/README.md
new file mode 100644
index 0000000..efd46e9
--- /dev/null
+++ b/course/Full Algorithmic Trading Using Python/README.md	
@@ -0,0 +1,37 @@
+# Full Algorithmic Trading Using Python — 课程教程
+
+> 本目录是对 YouTube 频道 **TradeOptionsWithMe** 的《Full Algorithmic Trading Using Python》系列的**学习笔记 + 视频归档**。
+> 播放列表：https://youtube.com/playlist?list=PLtqRgJ_TIq8Y6YG8-G-ETIFW_36mvxMLad
+
+- **集数**：19 集 ｜ **总时长**：约 6 小时 45 分
+- **每集内容**：📌 中文要点导读（提炼） + 📝 英文完整文稿（本地 ASR 转写） + 🎬 本地视频（360p）
+- **提醒**：文稿由自动语音识别生成，**未人工校对**；仅供个人学习检索，版权归原作者所有。视频画质上限 360p（YouTube 未登录下载限制）。
+
+## 目录
+
+| 集 | 标题 | 时长 | 链接 |
+|---|---|---|---|
+| 01 | Introduction（课程总览） | 7:19 | [📄 笔记](01-introduction.md) · [🎬 视频](videos/01-introduction.mp4) |
+| 02 | How Trading Algorithms Work（算法原理与开发流程） | 10:03 | [📄 笔记](02-episode-2.md) · [🎬 视频](videos/02-episode-2.mp4) |
+| 03 | Key Concepts（关键概念：时间处理·骨架·Symbol） | 10:05 | [📄 笔记](03-key-concepts.md) · [🎬 视频](videos/03-key-concepts.mp4) |
+| 04 | Handling Data（处理数据 · 第一个算法） | 18:01 | [📄 笔记](04-handling-data.md) · [🎬 视频](videos/04-handling-data.mp4) |
+| 05 | Trading & Orders（下单、订单管理与调试） | 28:46 | [📄 笔记](05-trading-and-orders.md) · [🎬 视频](videos/05-trading-and-orders.mp4) |
+| 06 | Indicators & Historical Data（指标与历史数据） | 28:32 | [📄 笔记](06-indicators-history.md) · [🎬 视频](videos/06-indicators-history.mp4) |
+| 07 | Consolidators & Rolling Windows（合并器、滚动窗口、事件调度） | 22:40 | [📄 笔记](07-consolidators-rolling-windows.md) · [🎬 视频](videos/07-consolidators-rolling-windows.mp4) |
+| 08 | Dynamic Universes（动态证券池与基本面选股） | 25:30 | [📄 笔记](08-dynamic-universes.md) · [🎬 视频](videos/08-dynamic-universes.mp4) |
+| 09 | Twitter Trading Bot（自定义数据 · 推文情绪） | 26:52 | [📄 笔记](09-twitter-trading-bot.md) · [🎬 视频](videos/09-twitter-trading-bot.mp4) |
+| 10 | Backtesting & Performance Analysis（回测与绩效评估） | 25:45 | [📄 笔记](10-backtesting-performance.md) · [🎬 视频](videos/10-backtesting-performance.mp4) |
+| 11 | Forex Trading（外汇 · 均值回归） | 13:27 | [📄 笔记](11-forex-trading.md) · [🎬 视频](videos/11-forex-trading.mp4) |
+| 12 | Options Trading（期权入门） | 15:28 | [📄 笔记](12-options-trading.md) · [🎬 视频](videos/12-options-trading.mp4) |
+| 13 | Options Code-Along（保护性看跌期权实战） | 26:06 | [📄 笔记](13-options-code-along.md) · [🎬 视频](videos/13-options-code-along.mp4) |
+| 14 | Crypto Trading Bots（加密货币 · RSI 动量） | 15:51 | [📄 笔记](14-crypto-trading-bots.md) · [🎬 视频](videos/14-crypto-trading-bots.mp4) |
+| 15 | The Algorithm Framework（算法框架） | 34:14 | [📄 笔记](15-algorithm-framework.md) · [🎬 视频](videos/15-algorithm-framework.mp4) |
+| 16 | Data-Driven Research（研究环境 · QuantBook） | 21:52 | [📄 笔记](16-data-driven-research.md) · [🎬 视频](videos/16-data-driven-research.mp4) |
+| 17 | Bitcoin ML Bot（用神经网络做比特币） | 31:07 | [📄 笔记](17-bitcoin-ml-bot.md) · [🎬 视频](videos/17-bitcoin-ml-bot.mp4) |
+| 18 | How to Live Trade（实盘部署与 Paper Trading） | 26:03 | [📄 笔记](18-live-trading.md) · [🎬 视频](videos/18-live-trading.mp4) |
+| 19 | 50-Line Trading Bot（50 行代码 · 标普成分股调整） | 17:29 | [📄 笔记](19-trading-bot-50-lines.md) · [🎬 视频](videos/19-trading-bot-50-lines.mp4) |
+
+## 关于本归档
+- 视频：经 yt-dlp 下载归档（360p），讲稿：faster-whisper 本地转写，导读：小五整理。
+- 系列主线为 **Python + QuantConnect（Lean 引擎）** 开发算法交易：从概念 → 下单/指标/数据 → 各类资产（股/汇/期权/币）→ 框架/研究/ML → 实盘。
+- 生成日期：2026-09-12。
diff --git a/course/videos/01-introduction.mp4 b/course/Full Algorithmic Trading Using Python/videos/01-introduction.mp4
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diff --git a/course/videos/03-key-concepts.mp4 b/course/Full Algorithmic Trading Using Python/videos/03-key-concepts.mp4
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diff --git a/course/videos/05-trading-and-orders.mp4 b/course/Full Algorithmic Trading Using Python/videos/05-trading-and-orders.mp4
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diff --git a/course/videos/06-indicators-history.mp4 b/course/Full Algorithmic Trading Using Python/videos/06-indicators-history.mp4
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diff --git a/course/videos/07-consolidators-rolling-windows.mp4 b/course/Full Algorithmic Trading Using Python/videos/07-consolidators-rolling-windows.mp4
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diff --git a/course/videos/08-dynamic-universes.mp4 b/course/Full Algorithmic Trading Using Python/videos/08-dynamic-universes.mp4
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diff --git a/course/videos/09-twitter-trading-bot.mp4 b/course/Full Algorithmic Trading Using Python/videos/09-twitter-trading-bot.mp4
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diff --git a/course/videos/10-backtesting-performance.mp4 b/course/Full Algorithmic Trading Using Python/videos/10-backtesting-performance.mp4
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diff --git a/course/videos/11-forex-trading.mp4 b/course/Full Algorithmic Trading Using Python/videos/11-forex-trading.mp4
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diff --git a/course/videos/12-options-trading.mp4 b/course/Full Algorithmic Trading Using Python/videos/12-options-trading.mp4
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diff --git a/course/videos/14-crypto-trading-bots.mp4 b/course/Full Algorithmic Trading Using Python/videos/14-crypto-trading-bots.mp4
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diff --git a/course/videos/15-algorithm-framework.mp4 b/course/Full Algorithmic Trading Using Python/videos/15-algorithm-framework.mp4
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diff --git a/course/videos/16-data-driven-research.mp4 b/course/Full Algorithmic Trading Using Python/videos/16-data-driven-research.mp4
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diff --git a/course/videos/17-bitcoin-ml-bot.mp4 b/course/Full Algorithmic Trading Using Python/videos/17-bitcoin-ml-bot.mp4
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diff --git a/course/videos/18-live-trading.mp4 b/course/Full Algorithmic Trading Using Python/videos/18-live-trading.mp4
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diff --git a/course/videos/19-trading-bot-50-lines.mp4 b/course/Full Algorithmic Trading Using Python/videos/19-trading-bot-50-lines.mp4
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diff --git a/course/README.md b/course/README.md
index efd46e9..5054ebd 100644
--- a/course/README.md
+++ b/course/README.md
@@ -1,37 +1,20 @@
-# Full Algorithmic Trading Using Python — 课程教程
+# course — 量化学习课程与资料
 
-> 本目录是对 YouTube 频道 **TradeOptionsWithMe** 的《Full Algorithmic Trading Using Python》系列的**学习笔记 + 视频归档**。
-> 播放列表：https://youtube.com/playlist?list=PLtqRgJ_TIq8Y6YG8-G-ETIFW_36mvxMLad
+本目录按「课程 + 资料 + 练习」分类归档。
 
-- **集数**：19 集 ｜ **总时长**：约 6 小时 45 分
-- **每集内容**：📌 中文要点导读（提炼） + 📝 英文完整文稿（本地 ASR 转写） + 🎬 本地视频（360p）
-- **提醒**：文稿由自动语音识别生成，**未人工校对**；仅供个人学习检索，版权归原作者所有。视频画质上限 360p（YouTube 未登录下载限制）。
+## 目录结构
 
-## 目录
+```
+course/
+├── Full Algorithmic Trading Using Python/   # 视频课程（19集，笔记+视频存档）
+├── 学习资料/                                 # 学习资料总表 / 视频候选清单
+└── 动手练习/                                 # 可运行的 A 股量化练习代码
+```
 
-| 集 | 标题 | 时长 | 链接 |
-|---|---|---|---|
-| 01 | Introduction（课程总览） | 7:19 | [📄 笔记](01-introduction.md) · [🎬 视频](videos/01-introduction.mp4) |
-| 02 | How Trading Algorithms Work（算法原理与开发流程） | 10:03 | [📄 笔记](02-episode-2.md) · [🎬 视频](videos/02-episode-2.mp4) |
-| 03 | Key Concepts（关键概念：时间处理·骨架·Symbol） | 10:05 | [📄 笔记](03-key-concepts.md) · [🎬 视频](videos/03-key-concepts.mp4) |
-| 04 | Handling Data（处理数据 · 第一个算法） | 18:01 | [📄 笔记](04-handling-data.md) · [🎬 视频](videos/04-handling-data.mp4) |
-| 05 | Trading & Orders（下单、订单管理与调试） | 28:46 | [📄 笔记](05-trading-and-orders.md) · [🎬 视频](videos/05-trading-and-orders.mp4) |
-| 06 | Indicators & Historical Data（指标与历史数据） | 28:32 | [📄 笔记](06-indicators-history.md) · [🎬 视频](videos/06-indicators-history.mp4) |
-| 07 | Consolidators & Rolling Windows（合并器、滚动窗口、事件调度） | 22:40 | [📄 笔记](07-consolidators-rolling-windows.md) · [🎬 视频](videos/07-consolidators-rolling-windows.mp4) |
-| 08 | Dynamic Universes（动态证券池与基本面选股） | 25:30 | [📄 笔记](08-dynamic-universes.md) · [🎬 视频](videos/08-dynamic-universes.mp4) |
-| 09 | Twitter Trading Bot（自定义数据 · 推文情绪） | 26:52 | [📄 笔记](09-twitter-trading-bot.md) · [🎬 视频](videos/09-twitter-trading-bot.mp4) |
-| 10 | Backtesting & Performance Analysis（回测与绩效评估） | 25:45 | [📄 笔记](10-backtesting-performance.md) · [🎬 视频](videos/10-backtesting-performance.mp4) |
-| 11 | Forex Trading（外汇 · 均值回归） | 13:27 | [📄 笔记](11-forex-trading.md) · [🎬 视频](videos/11-forex-trading.mp4) |
-| 12 | Options Trading（期权入门） | 15:28 | [📄 笔记](12-options-trading.md) · [🎬 视频](videos/12-options-trading.mp4) |
-| 13 | Options Code-Along（保护性看跌期权实战） | 26:06 | [📄 笔记](13-options-code-along.md) · [🎬 视频](videos/13-options-code-along.mp4) |
-| 14 | Crypto Trading Bots（加密货币 · RSI 动量） | 15:51 | [📄 笔记](14-crypto-trading-bots.md) · [🎬 视频](videos/14-crypto-trading-bots.mp4) |
-| 15 | The Algorithm Framework（算法框架） | 34:14 | [📄 笔记](15-algorithm-framework.md) · [🎬 视频](videos/15-algorithm-framework.mp4) |
-| 16 | Data-Driven Research（研究环境 · QuantBook） | 21:52 | [📄 笔记](16-data-driven-research.md) · [🎬 视频](videos/16-data-driven-research.mp4) |
-| 17 | Bitcoin ML Bot（用神经网络做比特币） | 31:07 | [📄 笔记](17-bitcoin-ml-bot.md) · [🎬 视频](videos/17-bitcoin-ml-bot.mp4) |
-| 18 | How to Live Trade（实盘部署与 Paper Trading） | 26:03 | [📄 笔记](18-live-trading.md) · [🎬 视频](videos/18-live-trading.mp4) |
-| 19 | 50-Line Trading Bot（50 行代码 · 标普成分股调整） | 17:29 | [📄 笔记](19-trading-bot-50-lines.md) · [🎬 视频](videos/19-trading-bot-50-lines.mp4) |
+| 分类 | 说明 |
+|---|---|
+| `Full Algorithmic Trading Using Python/` | YouTube「TradeOptionsWithMe」系列全套：19 集中文要点笔记 + 英文转写 + 本地视频 |
+| `学习资料/` | `资料总表.md`（书单/平台/框架/社区）、`视频候选清单.md`（B站待下载候选） |
+| `动手练习/` | 01 pandas热身 · 02 akshare取数 · 03 双均线回测 · 04 动量轮动 · 05 多因子；含 `lib_quant.py`、`run.sh` |
 
-## 关于本归档
-- 视频：经 yt-dlp 下载归档（360p），讲稿：faster-whisper 本地转写，导读：小五整理。
-- 系列主线为 **Python + QuantConnect（Lean 引擎）** 开发算法交易：从概念 → 下单/指标/数据 → 各类资产（股/汇/期权/币）→ 框架/研究/ML → 实盘。
-- 生成日期：2026-09-12。
+> 整理：小五 | 2026-09-28
diff --git a/course/动手练习/01_pandas_热身.py b/course/动手练习/01_pandas_热身.py
new file mode 100644
index 0000000..f14bb59
--- /dev/null
+++ b/course/动手练习/01_pandas_热身.py
@@ -0,0 +1,26 @@
+"""01 | pandas 热身：收益率、波动、净值曲线
+目标：搞懂量化里最基础的三个东西——收益、风险、净值。
+"""
+import pandas as pd, numpy as np
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from lib_quant import get_daily, add_returns, net_value, stats
+
+df = get_daily("600519", "20200101", "20241231")   # 贵州茅台
+df = add_returns(df)
+nv = net_value(df["ret"])
+print("==", "600519 贵州茅台", "==")
+print(df[["日期", "收盘", "ret"]].tail(3).to_string(index=False))
+print("绩效:", stats(df["ret"]))
+
+plt.figure(figsize=(9, 4))
+plt.plot(df["日期"], nv)
+plt.title("Net value (buy & hold)")
+plt.xlabel("date"); plt.ylabel("nav")
+plt.tight_layout(); plt.savefig("../data/01_net_value.png", dpi=110)
+print("图已存 data/01_net_value.png")
+
+# 思考题：
+# 1) 为什么不看"价格涨了多少"，而要看"收益率"和"净值"？
+# 2) 日收益率为 0 的日子占多少？(df['ret'].eq(0).mean())
diff --git a/course/动手练习/02_拉数据_akshare.py b/course/动手练习/02_拉数据_akshare.py
new file mode 100644
index 0000000..e9be00d
--- /dev/null
+++ b/course/动手练习/02_拉数据_akshare.py
@@ -0,0 +1,19 @@
+"""02 | 用 akshare 拉数据并落地缓存
+目标：掌握免费 A股数据源用法——这是量化的一切起点。
+"""
+from lib_quant import get_daily, CACHE
+import os
+
+for code, name in [("000001", "平安银行"), ("600519", "贵州茅台"), ("300750", "宁德时代")]:
+    df = get_daily(code, "20230101", "20241231")
+    d0 = str(df["日期"].iloc[0])[:10]
+    d1 = str(df["日期"].iloc[-1])[:10]
+    print(f"{code} {name}: {len(df)} 行 | {d0} ~ {d1}")
+    print("  列:", list(df.columns))
+
+print("\n缓存目录:", os.path.abspath(CACHE))
+print("文件:", sorted(os.listdir(CACHE)))
+
+# 思考题：
+# 1) 前复权(qfq)/后复权(hfq)/不复权 区别？算收益率该用哪个？(改 adjust 参数对比)
+# 2) 每日数据延迟多久？实盘能用吗？(提示: akshare 文档 + 交易所规则)
diff --git a/course/动手练习/03_双均线回测.py b/course/动手练习/03_双均线回测.py
new file mode 100644
index 0000000..0dc5d1e
--- /dev/null
+++ b/course/动手练习/03_双均线回测.py
@@ -0,0 +1,33 @@
+"""03 | 双均线策略 + 回测
+目标：写出人生第一个完整策略，并做归因。
+策略：5日均线上穿20日均线买入，下穿卖出。
+"""
+import pandas as pd, numpy as np
+import matplotlib; matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from lib_quant import get_daily, add_returns, net_value, stats
+
+FAST, SLOW = 5, 20
+df = get_daily("600519", "20200101", "20241231")
+df = add_returns(df)
+df["ma_fast"] = df["收盘"].rolling(FAST).mean()
+df["ma_slow"] = df["收盘"].rolling(SLOW).mean()
+df["signal"] = (df["ma_fast"] > df["ma_slow"]).astype(int)      # 1=持有 0=空仓
+df["pos"] = df["signal"].shift(1).fillna(0)                     # 次日开盘才算，防未来函数
+df["strat_ret"] = df["pos"] * df["ret"]
+
+nv_bh, nv_st = net_value(df["ret"]), net_value(df["strat_ret"])
+print("== 买入持有 ==", stats(df["ret"]))
+print("== 双均线   ==", stats(df["strat_ret"]))
+
+plt.figure(figsize=(10, 4))
+plt.plot(df["日期"], nv_bh, label="BuyHold")
+plt.plot(df["日期"], nv_st, label=f"MA{FAST}/{SLOW}")
+plt.legend(); plt.title("Strategy vs Buy&Hold"); plt.tight_layout()
+plt.savefig("../data/03_dual_ma.png", dpi=110)
+print("图已存 data/03_dual_ma.png")
+
+# 思考题：
+# 1) 为什么 pos 要 shift(1)？不 shift 会怎样？(把 shift(1) 去掉对比——这叫"未来函数")
+# 2) 改 FAST/SLOW 参数，结果稳吗？换 (10,60) 试试——这叫参数敏感性
+# 3) 这策略赢过买入持有了吗？为什么？
diff --git a/course/动手练习/04_动量轮动.py b/course/动手练习/04_动量轮动.py
new file mode 100644
index 0000000..41c30e9
--- /dev/null
+++ b/course/动手练习/04_动量轮动.py
@@ -0,0 +1,36 @@
+"""04 | 动量轮动策略
+目标：从"单票择时"升级到"多标的轮动"。
+策略：每月末看过去 N 日涨幅，持有最强的一只。
+"""
+import pandas as pd, numpy as np
+from lib_quant import get_daily, net_value, stats
+
+POOL = {"600519": "贵州茅台", "000001": "平安银行", "300750": "宁德时代",
+        "601318": "中国平安", "000858": "五粮液"}
+LOOKBACK, FREQ = 20, 20   # 回看20日，每20个交易日调仓
+
+px = {}
+for c in POOL:
+    d = get_daily(c, "20200101", "20241231")[["日期", "收盘"]].set_index("日期")
+    px[c] = d["收盘"]
+prices = pd.DataFrame(px).dropna()
+mom = prices.pct_change(LOOKBACK)
+
+rets = []
+cur = None
+for i in range(LOOKBACK, len(prices) - 1):
+    if (i - LOOKBACK) % FREQ == 0:                 # 调仓日
+        cur = mom.iloc[i].idxmax()                 # 动量最强
+    if cur is not None:
+        r = prices[cur].iloc[i + 1] / prices[cur].iloc[i] - 1
+        rets.append(r)
+    else:
+        rets.append(0.0)
+
+s = pd.Series(rets)
+print("== 动量轮动 ==", stats(s))
+print("== 等权买入持有 ==", stats(prices.pct_change().mean(axis=1).iloc[LOOKBACK:]))
+
+# 思考题：
+# 1) 换 LOOKBACK (10/60/120) 看结果——动量在A股有效吗？
+# 2) 为什么不持有全部、按动量加权？(提示: 集中 vs 分散)
diff --git a/course/动手练习/05_多因子选股.py b/course/动手练习/05_多因子选股.py
new file mode 100644
index 0000000..bac3442
--- /dev/null
+++ b/course/动手练习/05_多因子选股.py
@@ -0,0 +1,37 @@
+"""05 | 多因子选股（横截面）
+目标：体验"因子"最朴素的样子——按指标排序选股。
+数据：akshare 全A快照(含市盈率/市净率等)。
+"""
+import time
+import akshare as ak, pandas as pd
+
+def fetch_spot(retries=6):
+    last = None
+    for i in range(retries):
+        try:
+            return ak.stock_zh_a_spot_em()
+        except Exception as e:
+            last = e; time.sleep(3 + i * 3)
+    raise last
+
+try:
+    spot = fetch_spot()
+    print("全A快照:", spot.shape)
+    df = spot.copy()
+    pe = pd.to_numeric(df.get("市盈率-动态"), errors="coerce")
+    df = df[pe.notna()].copy()
+    df["EP"] = 1 / pe[pe.notna()]                 # 盈利收益率(1/PE)
+    df["EP_rank"] = df["EP"].rank(pct=True)
+    top = df.sort_values("EP_rank", ascending=False).head(20)[
+        ["代码", "名称", "最新价", "市盈率-动态", "EP_rank"]]
+    print("\n== 按盈利收益率(EP)选出的前20（低估值）==")
+    print(top.to_string(index=False))
+    top.to_csv("../data/05_top20_EP.csv", index=False, encoding="utf-8-sig")
+    print("已存 data/05_top20_EP.csv")
+except Exception as e:
+    print("取数失败(网络/接口变动):", repr(e)[:200])
+    print("可稍后重跑；或先跑 01-04（已缓存，离线可跑）")
+
+# 思考题：
+# 1) 单因子排序选股有什么坑？(行业偏差/市值偏差/未来函数)
+# 2) 怎么把2个因子合成打分？(标准化后加权，试试 z-score)
diff --git a/course/动手练习/README.md b/course/动手练习/README.md
new file mode 100644
index 0000000..17cb687
--- /dev/null
+++ b/course/动手练习/README.md
@@ -0,0 +1,23 @@
+# 量化动手练习（用真实 A 股数据）
+
+> 目标：把"看书看不进去"的瓶颈，换成**能跑出结果**的闭环练习。
+> 环境：Python 3.11 + pandas/akshare/backtrader/matplotlib（已装在 `../pylibs`）
+> 运行：`bash run.sh`（全部）或 `bash run.sh 03`（单课）
+
+## 怎么用
+每一课 = 目标 + 代码 + 思考题。先跑通，再改参数看结果变化，最后回答思考题。卡住直接问小五。
+
+## 课程表
+| 课 | 文件 | 主题 | 你会学到 |
+|---|---|---|---|
+| 01 | 01_pandas_热身.py | 收益率/波动/净值 | 量化最基础的语言 |
+| 02 | 02_拉数据_akshare.py | 用 akshare 拉 A股数据 | 免费数据源怎么用 |
+| 03 | 03_双均线回测.py | 双均线策略+回测 | 一个完整策略长什么样 |
+| 04 | 04_动量轮动.py | 动量轮动策略 | 多标的轮动调仓 |
+| 05 | 05_多因子选股.py | 多因子选股 | 从横截面数据里选股 |
+
+## 数据
+- 默认用 akshare 在线拉（国内可达），首次拉完自动缓存到 `../data/cache/*.csv`
+
+## 学习闭环
+跑通 → 改参数 → 想为什么 → 写结论 → 告诉小五批改/加题
diff --git a/course/动手练习/lib_quant.py b/course/动手练习/lib_quant.py
new file mode 100644
index 0000000..614560d
--- /dev/null
+++ b/course/动手练习/lib_quant.py
@@ -0,0 +1,48 @@
+"""量化练习公共库：数据获取(带缓存+重试) + 常用指标 + 回测指标。"""
+import os, time, pandas as pd, numpy as np
+
+CACHE = os.path.join(os.path.dirname(__file__), "..", "data", "cache")
+os.makedirs(CACHE, exist_ok=True)
+
+def _fetch(symbol, start, end, adjust, retries=6, wait=2.0):
+    import akshare as ak
+    last = None
+    for i in range(retries):
+        try:
+            return ak.stock_zh_a_hist(symbol=symbol, period="daily",
+                                      start_date=start, end_date=end, adjust=adjust)
+        except Exception as e:                       # 网络间歇性抽风，重试
+            last = e
+            time.sleep(wait * (i + 1))
+    raise last
+
+def get_daily(symbol="000001", start="20200101", end="20241231", adjust="qfq"):
+    """拉单只A股日线(前复权)，带本地缓存与自动重试。"""
+    f = os.path.join(CACHE, f"{symbol}_{start}_{end}_{adjust}.csv")
+    if os.path.exists(f):
+        return pd.read_csv(f, parse_dates=["日期"])
+    df = _fetch(symbol, start, end, adjust)
+    df.to_csv(f, index=False, encoding="utf-8-sig")
+    return df
+
+def add_returns(df, price_col="收盘"):
+    df = df.copy()
+    df["ret"] = df[price_col].pct_change().fillna(0.0)
+    return df
+
+def net_value(ret):
+    return (1 + pd.Series(ret).fillna(0.0)).cumprod()
+
+def stats(ret, periods=252):
+    ret = pd.Series(ret).fillna(0.0)
+    nv = net_value(ret); total = nv.iloc[-1] - 1
+    ann = (1 + total) ** (periods / max(len(ret), 1)) - 1
+    vol = ret.std() * np.sqrt(periods)
+    sharpe = (ret.mean() * periods) / vol if vol > 0 else np.nan
+    dd = (nv / nv.cummax() - 1).min()
+    calmar = ann / abs(dd) if dd < 0 else np.nan
+    return {"总收益": f"{total:.2%}", "年化": f"{ann:.2%}", "年化波动": f"{vol:.2%}",
+            "夏普": f"{sharpe:.2f}", "最大回撤": f"{dd:.2%}", "卡玛": f"{calmar:.2f}"}
+
+def max_drawdown(nv):
+    return (nv / nv.cummax() - 1).min()
diff --git a/course/动手练习/run.sh b/course/动手练习/run.sh
new file mode 100644
index 0000000..7a6bdb1
--- /dev/null
+++ b/course/动手练习/run.sh
@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+# 一键跑练习：bash run.sh  或  bash run.sh 03
+cd "$(dirname "$0")"
+export PYTHONPATH="$(pwd)/../pylibs"
+export MPLCONFIGDIR="/tmp/mpl"
+mkdir -p ../data/cache
+case "$1" in
+  01|02|03|04|05) python3 ${1}_*.py ;;
+  *) for f in 0*.py; do echo; echo "############ $f ############"; python3 "$f"; done ;;
+esac
diff --git a/course/动手练习/seed_cache.py b/course/动手练习/seed_cache.py
new file mode 100644
index 0000000..b729b9f
--- /dev/null
+++ b/course/动手练习/seed_cache.py
@@ -0,0 +1,23 @@
+"""预热缓存：把练习需要的数据一次性拉齐(带重试+间隔)。之后离线可跑。"""
+import time, sys
+from lib_quant import get_daily, CACHE
+JOBS = [
+    ("600519","20200101","20241231"), ("600519","20230101","20241231"),
+    ("000001","20200101","20241231"), ("000001","20230101","20241231"),
+    ("300750","20200101","20241231"), ("300750","20230101","20241231"),
+    ("601318","20200101","20241231"), ("000858","20200101","20241231"),
+]
+for sym,s,e in JOBS:
+    import os
+    f=os.path.join(CACHE,f"{sym}_{s}_{e}_qfq.csv")
+    if os.path.exists(f):
+        print("cached", sym, s); continue
+    for att in range(8):
+        try:
+            df=get_daily(sym,s,e); print("OK", sym, s, len(df)); break
+        except Exception as ex:
+            print("retry",sym,att,repr(ex)[:60]); time.sleep(5+att*3)
+    else:
+        print("FAIL", sym, s); sys.exit(1)
+    time.sleep(3)
+print("ALL DONE")
diff --git a/course/学习资料/视频候选清单.md b/course/学习资料/视频候选清单.md
new file mode 100644
index 0000000..981301a
--- /dev/null
+++ b/course/学习资料/视频候选清单.md
@@ -0,0 +1,38 @@
+# 视频候选清单（确认后再下载）
+
+> 整理：小五 | 2026-09-28 | 来源：B站搜索实测（github/google 不通，走搜狗+必应国内）
+> 状态：**仅候选，未下载**。你勾选后我用 BBDown / yt-dlp 下到本地。
+
+## 一、推荐下载（值回票价）
+
+| # | 名称 | UP主 | 链接 | 类型 | 说明 |
+|---|---|---|---|---|---|
+| 1 | WorldQuant Brain《零基础学量化》AI版 第一课 | 水木人中 | https://www.bilibili.com/video/BV1d3oFBPES6 | 系统课 | 零基础成体系，从0讲策略 |
+| 2 | 《因子投资-方法与实践》石川 第1章 因子投资基础 | 量衍金工 | https://www.bilibili.com/video/BV1VxZcBqEsj | 读书课 | **石川《因子投资》**配套讲解，进阶重点 |
+| 3 | 《多因子策略实战》构建量化选股模型 | (投资经典精读) | https://www.bilibili.com/video/BV118at6ME3g | 读书课 | 多因子选股实战思路 |
+| 4 | 新手怎么开始学量化？认知篇：何为量化 | 龙猫说财 | https://www.bilibili.com/video/BV1Vtht62EPY | 入门 | 建立正确认知，避坑 |
+| 5 | 普通人如何从0开始做量化？(保姆级全流程) | QuantX西蒙斯 | https://www.bilibili.com/video/BV1JY8F61E6y | 入门 | 全流程走一遍 |
+| 6 | 5天学会 PTrade - AI协助策略 | 国金证券宜宾营业部 | https://www.bilibili.com/video/BV1aekKBkEVK | 券商课 | 券商官方，实盘环境入门 |
+| 7 | 怎样用 Deepseek Harness 做量化策略研究 | Mr看海 | https://www.bilibili.com/video/BV1Nzb367E5J | AI+量化 | 用LLM做研究，前沿 |
+| 8 | 【2026最新】Codex 量化实战教学 | 搞AI的阿黑 | https://www.bilibili.com/video/BV1jgtZ6fEHq | AI+量化 | 编程Agent做量化 |
+
+## 二、可作补充（先看一/再决定）
+
+| 名称 | UP主 | 链接 | 说明 |
+|---|---|---|---|
+| 横测：做量化最好用的大模型 | frank-quant | https://www.bilibili.com/video/BV1ijYB6QE1Y | 工具选型参考 |
+| 量化投资邢不行（系列） | 量化投资邢不行啊 | https://www.bilibili.com/video/BV1RZfxB1E68 | 拆解网红指标，防坑 |
+| 爆火Jev接入量化实盘实测 | 发明者量化 | https://www.bilibili.com/video/BV1kRez6BEuq | 实盘+AI |
+
+## 三、非B站课程（文本/视频混合）
+- 慕课网 / 网易云课堂：搜「Python 量化投资」— 有系统付费课
+- 中国大学 MOOC：高校《金融工程》《量化投资》公开课（免费）
+
+## 四、不推荐（已排除）
+- 币圈量化、跟单订阅、QMT连板超短、"永久躺平收益"类 —— 多为广告/割韭菜，已剔除
+
+## 五、下载方案（确认后执行）
+- 工具：**BBDown**（B站专用，支持 1080P/4K、字幕、合集）+ **yt-dlp**（通用）
+- 输出：`量化学习/data/videos/`，可合并音频+视频、下字幕
+- ⚠️ 付费/会员课程需自备账号；我这边无账号只能下免费公开视频
+- 需要的话我可以顺带**提取字幕**，转成文本给你看（更符合你的文本偏好）
diff --git a/course/学习资料/资料总表.md b/course/学习资料/资料总表.md
new file mode 100644
index 0000000..c20517f
--- /dev/null
+++ b/course/学习资料/资料总表.md
@@ -0,0 +1,53 @@
+# 量化投资学习资料总表（适合国内环境）
+
+> 汇总：小五 | 2026-09-28
+> 来源：小五主搜 + 小强(Gitea #31) + 小马(Gitea #30)（后者回帖后并入本表）
+> 说明：沙箱内 github/google 不通，链接优先给**国内可访问**的；GitHub 项目给 gitee 镜像路径。
+
+## A. 书单（文本 · 打地基）
+
+| 书名 | 作者 | 阶段 | 国内可购 | 备注 |
+|---|---|---|---|---|
+| 《打开量化投资的黑箱》第2版 | Rishi Narang | 入门 | ✅ 中文版 | 建立行业全貌，第一本 |
+| 《量化交易：如何建立自己的算法交易事业》 | Ernest Chan | 入门→实战 | ✅ 中文版 | 从0讲起，可照做 |
+| 《算法交易：制胜策略与原理》 | Ernest Chan | 进阶 | ✅ | 上本续作 |
+| 《Systematic Trading》 | Robert Carver | 进阶 | 英文为主 | 风险预算/系统化思想 |
+| 《主动投资组合管理》 | Grinold & Kahn | 进阶 | ✅ 中文版 | 因子/风险模型天花板 |
+| 《量化投资：策略与技术》 | 丁鹏 | 入门→进阶 | ✅ | 国内教材，A股语境 |
+| 《Python金融大数据分析》 | Yves Hilpisch | 工具 | ✅ | 配合实战 |
+| 《海龟交易法则》 | Curtis Faith | 入门 | ✅ | 趋势跟踪思想 |
+| 《统计套利》 | Andrew Pole | 进阶 | ✅ | 配对/均值回归 |
+
+## B. 在线平台 / 开源框架（文本为主 · 国内可访问）
+
+| 名称 | 类型 | 链接 | 阶段 | 备注 |
+|---|---|---|---|---|
+| 聚宽 JoinQuant | 在线研究+回测+社区 | https://www.joinquant.com | 全阶段 | **最省事入口**，A股数据全、策略帖海量 |
+| 米筐 RiceQuant | 在线平台 | https://www.ricequant.com | 全阶段 | 同类 |
+| 掘金量化 | 在线平台 | https://www.myquant.cn | 全阶段 | 同类 |
+| BigQuant | 在线AI量化 | https://bigquant.com | 进阶 | 机器学习向 |
+| AKShare | 免费数据源(库) | https://akshare.akfamily.xyz | 数据 | ✅ 中文文档，A股/期货/基金 |
+| Tushare | 数据源(库) | https://tushare.pro | 数据 | 需注册积分 |
+| backtrader | 回测框架 | 文档 https://www.backtrader.com （中文教程多） | 回测 | 轻量易上手 |
+| vnpy | 交易框架 | https://www.vnpy.com | 实盘 | 国内最主流，配书《vn.py 从入门到进阶》 |
+| Qlib(微软) | AI量化投研 | github.com/microsoft/qlib（用 gitee 镜像） | 进阶/ML | 因子+模型全流程 |
+
+## C. 社区 / 专栏（文本）
+
+| 名称 | 链接 | 备注 |
+|---|---|---|
+| 知乎「量化投资学习路线图·书籍篇」等专栏 | https://zhuanlan.zhihu.com | 路线梳理 |
+| CSDN 量化专栏 | https://blog.csdn.net | 中文实战多 |
+| 掘金 | https://juejin.cn | 工程向 |
+| 聚宽/米筐社区帖 | 平台内 | 策略源码 |
+
+## D. 视频（候选见「视频候选清单.md」，确认后再下载）
+
+## E. 按阶段的路线（建议照走）
+1. **基础**：Python + pandas/numpy + 金融常识 + 统计学
+2. **数据+回测**：AKShare/Tushare + 聚宽/backtrader → 写双均线、动量
+3. **策略进阶**：多因子、统计套利、CTA、事件驱动
+4. **工程/ML**：Qlib、因子库、ML选股、风控
+5. **实盘**：vnpy 接券商/期货
+
+> 待办：小强(#31)、小马(#30) 回帖后并入本表并去重。