Add ipynb format check (#1439)

* Update test_qlib_from_source.yml * add ipynb format check to workflow * test ipynb CI * modify nbqa check path * add pylint flake8 mypy check to ipynb * check ipynb with black and pylint * reformat .ipynb files * format line length nbqa black . -l 120 * update nbqa .ipynb format CI * format old ipynb files * add nbconvert check to CI * adjust CI order to avoid repeating download data
2026-06-06 05:51:17 +08:00 · 2023-02-21 09:23:22 +08:00
parent 5eb5ac1f1f
commit 76f2fb1a1a
6 changed files with 275 additions and 173 deletions
--- a/.github/workflows/test_qlib_from_source.yml
+++ b/.github/workflows/test_qlib_from_source.yml
@@ -121,6 +121,11 @@ jobs:
      run: |
        mypy qlib --install-types --non-interactive || true
        mypy qlib --verbose
+    
+    - name: Check Qlib ipynb with nbqa
+      run: |
+        nbqa black . -l 120 --check --diff
+        nbqa pylint . --disable=C0104,C0114,C0115,C0116,C0301,C0302,C0411,C0413,C1802,R0401,R0801,R0902,R0903,R0911,R0912,R0913,R0914,R0915,R1720,W0105,W0123,W0201,W0511,W0613,W1113,W1514,E0401,E1121,C0103,C0209,R0402,R1705,R1710,R1725,R1735,W0102,W0212,W0221,W0223,W0231,W0237,W0612,W0621,W0622,W0703,W1309,E1102,E1136,W0719,W0104,W0404,C0412,W0611,C0410 --const-rgx='[a-z_][a-z0-9_]{2,30}$'

    - name: Test data downloads
      run: |
@@ -139,6 +144,12 @@ jobs:
        brew unlink libomp
        brew install libomp.rb

+    # Run after data downloads
+    - name: Check Qlib ipynb with nbconvert
+      run: |
+        # add more ipynb files in future
+        jupyter nbconvert --to notebook --execute examples/workflow_by_code.ipynb
+
    - name: Test workflow by config (install from source)
      run: |
        python -m pip install numba
--- a/examples/benchmarks/TRA/Reports.ipynb
+++ b/examples/benchmarks/TRA/Reports.ipynb
@@ -25,59 +25,65 @@
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
-    "sns.set(style='white')\n",
-    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
-    "matplotlib.rcParams['ps.fonttype'] = 42\n",
+    "\n",
+    "sns.set(style=\"white\")\n",
+    "matplotlib.rcParams[\"pdf.fonttype\"] = 42\n",
+    "matplotlib.rcParams[\"ps.fonttype\"] = 42\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "from joblib import Parallel, delayed\n",
    "\n",
+    "\n",
    "def func(x, N=80):\n",
    "    ret = x.ret.copy()\n",
    "    x = x.rank(pct=True)\n",
-    "    x['ret'] = ret\n",
+    "    x[\"ret\"] = ret\n",
    "    diff = x.score.sub(x.label)\n",
-    "    r = x.nlargest(N, columns='score').ret.mean()\n",
-    "    r -= x.nsmallest(N, columns='score').ret.mean()\n",
-    "    return pd.Series({\n",
-    "        'MSE': diff.pow(2).mean(), \n",
-    "        'MAE': diff.abs().mean(), \n",
-    "        'IC': x.score.corr(x.label),\n",
-    "        'R': r\n",
-    "    })\n",
-    "    \n",
+    "    r = x.nlargest(N, columns=\"score\").ret.mean()\n",
+    "    r -= x.nsmallest(N, columns=\"score\").ret.mean()\n",
+    "    return pd.Series(\n",
+    "        {\n",
+    "            \"MSE\": diff.pow(2).mean(),\n",
+    "            \"MAE\": diff.abs().mean(),\n",
+    "            \"IC\": x.score.corr(x.label),\n",
+    "            \"R\": r,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "\n",
    "ret = pd.read_pickle(\"data/ret.pkl\").clip(-0.1, 0.1)\n",
+    "\n",
+    "\n",
    "def backtest(fname, **kwargs):\n",
-    "    pred = pd.read_pickle(fname).loc['2018-09-21':'2020-06-30']  # test period\n",
-    "    pred['ret'] = ret\n",
+    "    pred = pd.read_pickle(fname).loc[\"2018-09-21\":\"2020-06-30\"]  # test period\n",
+    "    pred[\"ret\"] = ret\n",
    "    dates = pred.index.unique(level=0)\n",
    "    res = Parallel(n_jobs=-1)(delayed(func)(pred.loc[d], **kwargs) for d in dates)\n",
-    "    res = {\n",
-    "       dates[i]: res[i]\n",
-    "       for i in range(len(dates))\n",
-    "    }\n",
+    "    res = {dates[i]: res[i] for i in range(len(dates))}\n",
    "    res = pd.DataFrame(res).T\n",
-    "    r = res['R'].copy()\n",
+    "    r = res[\"R\"].copy()\n",
    "    r.index = pd.to_datetime(r.index)\n",
    "    r = r.reindex(pd.date_range(r.index[0], r.index[-1])).fillna(0)  # paper use 365 days\n",
    "    return {\n",
-    "        'MSE': res['MSE'].mean(),\n",
-    "        'MAE': res['MAE'].mean(),\n",
-    "        'IC': res['IC'].mean(),\n",
-    "        'ICIR': res['IC'].mean()/res['IC'].std(),\n",
-    "        'AR': r.mean()*365,\n",
-    "        'AV': r.std()*365**0.5,\n",
-    "        'SR': r.mean()/r.std()*365**0.5,\n",
-    "        'MDD': (r.cumsum().cummax() - r.cumsum()).max()\n",
+    "        \"MSE\": res[\"MSE\"].mean(),\n",
+    "        \"MAE\": res[\"MAE\"].mean(),\n",
+    "        \"IC\": res[\"IC\"].mean(),\n",
+    "        \"ICIR\": res[\"IC\"].mean() / res[\"IC\"].std(),\n",
+    "        \"AR\": r.mean() * 365,\n",
+    "        \"AV\": r.std() * 365**0.5,\n",
+    "        \"SR\": r.mean() / r.std() * 365**0.5,\n",
+    "        \"MDD\": (r.cumsum().cummax() - r.cumsum()).max(),\n",
    "    }, r\n",
    "\n",
+    "\n",
    "def fmt(x, p=3, scale=1, std=False):\n",
-    "    _fmt = '{:.%df}'%p\n",
+    "    _fmt = \"{:.%df}\" % p\n",
    "    string = _fmt.format((x.mean() if not isinstance(x, (float, np.floating)) else x) * scale)\n",
    "    if std and len(x) > 1:\n",
-    "        string += ' ('+_fmt.format(x.std()*scale)+')'\n",
+    "        string += \" (\" + _fmt.format(x.std() * scale) + \")\"\n",
    "    return string\n",
    "\n",
+    "\n",
    "def backtest_multi(files, **kwargs):\n",
    "    res = []\n",
    "    pnl = []\n",
@@ -88,14 +94,14 @@
    "    res = pd.DataFrame(res)\n",
    "    pnl = pd.concat(pnl, axis=1)\n",
    "    return {\n",
-    "        'MSE': fmt(res['MSE'], std=True),\n",
-    "        'MAE': fmt(res['MAE'], std=True),\n",
-    "        'IC': fmt(res['IC']),\n",
-    "        'ICIR': fmt(res['ICIR']),\n",
-    "        'AR': fmt(res['AR'], scale=100, p=1)+'%',\n",
-    "        'VR': fmt(res['AV'], scale=100, p=1)+'%',\n",
-    "        'SR': fmt(res['SR']),\n",
-    "        'MDD': fmt(res['MDD'], scale=100, p=1)+'%'\n",
+    "        \"MSE\": fmt(res[\"MSE\"], std=True),\n",
+    "        \"MAE\": fmt(res[\"MAE\"], std=True),\n",
+    "        \"IC\": fmt(res[\"IC\"]),\n",
+    "        \"ICIR\": fmt(res[\"ICIR\"]),\n",
+    "        \"AR\": fmt(res[\"AR\"], scale=100, p=1) + \"%\",\n",
+    "        \"VR\": fmt(res[\"AV\"], scale=100, p=1) + \"%\",\n",
+    "        \"SR\": fmt(res[\"SR\"]),\n",
+    "        \"MDD\": fmt(res[\"MDD\"], scale=100, p=1) + \"%\",\n",
    "    }, pnl"
   ]
  },
@@ -124,16 +130,20 @@
   "outputs": [],
   "source": [
    "exps = {\n",
-    "    'Linear': ['output/Linear/pred.pkl'],\n",
-    "    'LightGBM': ['output/GBDT/lr0.05_leaves128/pred.pkl'],\n",
-    "    'MLP': glob.glob('output/search/MLP/hs128_bs512_do0.3_lr0.001_seed*/pred.pkl'),\n",
-    "    'SFM': glob.glob('output/search/SFM/hs32_bs512_do0.5_lr0.001_seed*/pred.pkl'),\n",
-    "    'ALSTM': glob.glob('output/search/LSTM_Attn/hs256_bs1024_do0.1_lr0.0002_seed*/pred.pkl'),\n",
-    "    'Trans.': glob.glob('output/search/Transformer/head4_hs64_bs1024_do0.1_lr0.0002_seed*/pred.pkl'),\n",
-    "    'ALSTM+TS':glob.glob('output/LSTM_Attn_TS/hs256_bs1024_do0.1_lr0.0002_seed*/pred.pkl'),\n",
-    "    'Trans.+TS':glob.glob('output/Transformer_TS/head4_hs64_bs1024_do0.1_lr0.0002_seed*/pred.pkl'),\n",
-    "    'ALSTM+TRA(Ours)': glob.glob('output/search/finetune/LSTM_Attn_tra/K10_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl'),\n",
-    "    'Trans.+TRA(Ours)': glob.glob('output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb1.0_head4_hs64_bs512_do0.1_lr0.0005_seed*/pred.pkl')\n",
+    "    \"Linear\": [\"output/Linear/pred.pkl\"],\n",
+    "    \"LightGBM\": [\"output/GBDT/lr0.05_leaves128/pred.pkl\"],\n",
+    "    \"MLP\": glob.glob(\"output/search/MLP/hs128_bs512_do0.3_lr0.001_seed*/pred.pkl\"),\n",
+    "    \"SFM\": glob.glob(\"output/search/SFM/hs32_bs512_do0.5_lr0.001_seed*/pred.pkl\"),\n",
+    "    \"ALSTM\": glob.glob(\"output/search/LSTM_Attn/hs256_bs1024_do0.1_lr0.0002_seed*/pred.pkl\"),\n",
+    "    \"Trans.\": glob.glob(\"output/search/Transformer/head4_hs64_bs1024_do0.1_lr0.0002_seed*/pred.pkl\"),\n",
+    "    \"ALSTM+TS\": glob.glob(\"output/LSTM_Attn_TS/hs256_bs1024_do0.1_lr0.0002_seed*/pred.pkl\"),\n",
+    "    \"Trans.+TS\": glob.glob(\"output/Transformer_TS/head4_hs64_bs1024_do0.1_lr0.0002_seed*/pred.pkl\"),\n",
+    "    \"ALSTM+TRA(Ours)\": glob.glob(\n",
+    "        \"output/search/finetune/LSTM_Attn_tra/K10_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl\"\n",
+    "    ),\n",
+    "    \"Trans.+TRA(Ours)\": glob.glob(\n",
+    "        \"output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb1.0_head4_hs64_bs512_do0.1_lr0.0005_seed*/pred.pkl\"\n",
+    "    ),\n",
    "}"
   ]
  },
@@ -160,14 +170,8 @@
    }
   ],
   "source": [
-    "res = {\n",
-    "    name: backtest_multi(exps[name])\n",
-    "    for name in tqdm(exps)\n",
-    "}\n",
-    "report = pd.DataFrame({\n",
-    "    k: v[0]\n",
-    "    for k, v in res.items()\n",
-    "}).T"
+    "res = {name: backtest_multi(exps[name]) for name in tqdm(exps)}\n",
+    "report = pd.DataFrame({k: v[0] for k, v in res.items()}).T"
   ]
  },
  {
@@ -385,24 +389,40 @@
    }
   ],
   "source": [
-    "df = pd.read_pickle('output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb0.0_head4_hs64_bs512_do0.1_lr0.0005_seed1000/pred.pkl')\n",
-    "code = 'SH600157'\n",
-    "date = '2018-09-28'\n",
+    "df = pd.read_pickle(\n",
+    "    \"output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb0.0_head4_hs64_bs512_do0.1_lr0.0005_seed1000/pred.pkl\"\n",
+    ")\n",
+    "code = \"SH600157\"\n",
+    "date = \"2018-09-28\"\n",
    "lookbackperiod = 50\n",
    "\n",
    "prob = df.iloc[:, -3:].loc(axis=0)[:, code].reset_index(level=1, drop=True).loc[date:].iloc[:lookbackperiod]\n",
-    "pred = df.loc[:,[\"score_0\",\"score_1\",\"score_2\",\"label\"]].loc(axis=0)[:, code].reset_index(level=1, drop=True).loc[date:].iloc[:lookbackperiod]\n",
-    "e_all = pred.iloc[:,:-1].sub(pred.iloc[:,-1], axis=0).pow(2)\n",
+    "pred = (\n",
+    "    df.loc[:, [\"score_0\", \"score_1\", \"score_2\", \"label\"]]\n",
+    "    .loc(axis=0)[:, code]\n",
+    "    .reset_index(level=1, drop=True)\n",
+    "    .loc[date:]\n",
+    "    .iloc[:lookbackperiod]\n",
+    ")\n",
+    "e_all = pred.iloc[:, :-1].sub(pred.iloc[:, -1], axis=0).pow(2)\n",
    "e_all = e_all.sub(e_all.min(axis=1), axis=0)\n",
-    "e_all.columns = [r'$\\theta_%d$'%d for d in range(1, 4)]\n",
+    "e_all.columns = [r\"$\\theta_%d$\" % d for d in range(1, 4)]\n",
    "prob = pd.Series(np.argmax(prob.values, axis=1), index=prob.index).rolling(7).mean().round()\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(7, 3))\n",
-    "e_all.plot(ax=axes[0], xlabel='', rot=30)\n",
-    "prob.plot(ax=axes[1], xlabel='', rot=30, color='red', linestyle='None', marker='^', markersize=5)\n",
+    "e_all.plot(ax=axes[0], xlabel=\"\", rot=30)\n",
+    "prob.plot(\n",
+    "    ax=axes[1],\n",
+    "    xlabel=\"\",\n",
+    "    rot=30,\n",
+    "    color=\"red\",\n",
+    "    linestyle=\"None\",\n",
+    "    marker=\"^\",\n",
+    "    markersize=5,\n",
+    ")\n",
    "plt.yticks(np.array([0, 1, 2]), e_all.columns.values)\n",
-    "axes[0].set_ylabel('Predictor Loss')\n",
-    "axes[1].set_ylabel('Router Selection')\n",
+    "axes[0].set_ylabel(\"Predictor Loss\")\n",
+    "axes[1].set_ylabel(\"Router Selection\")\n",
    "plt.tight_layout()\n",
    "# plt.savefig('select.pdf', bbox_inches='tight')\n",
    "plt.show()"
@@ -428,10 +448,18 @@
   "outputs": [],
   "source": [
    "exps = {\n",
-    "    'Random': glob.glob('output/search/LSTM_Attn_tra/K10_traHs16_traSrcNONE_traLamb1.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl'),\n",
-    "    'LR': glob.glob('output/search/LSTM_Attn_tra/K10_traHs16_traSrcLR_traLamb1.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl'),\n",
-    "    'TPE': glob.glob('output/search/LSTM_Attn_tra/K10_traHs16_traSrcTPE_traLamb1.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl'),\n",
-    "    'LR+TPE': glob.glob('output/search/finetune/LSTM_Attn_tra/K10_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl')\n",
+    "    \"Random\": glob.glob(\n",
+    "        \"output/search/LSTM_Attn_tra/K10_traHs16_traSrcNONE_traLamb1.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl\"\n",
+    "    ),\n",
+    "    \"LR\": glob.glob(\n",
+    "        \"output/search/LSTM_Attn_tra/K10_traHs16_traSrcLR_traLamb1.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl\"\n",
+    "    ),\n",
+    "    \"TPE\": glob.glob(\n",
+    "        \"output/search/LSTM_Attn_tra/K10_traHs16_traSrcTPE_traLamb1.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl\"\n",
+    "    ),\n",
+    "    \"LR+TPE\": glob.glob(\n",
+    "        \"output/search/finetune/LSTM_Attn_tra/K10_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl\"\n",
+    "    ),\n",
    "}"
   ]
  },
@@ -456,14 +484,8 @@
    }
   ],
   "source": [
-    "res = {\n",
-    "    name: backtest_multi(exps[name])\n",
-    "    for name in tqdm(exps)\n",
-    "}\n",
-    "report = pd.DataFrame({\n",
-    "    k: v[0]\n",
-    "    for k, v in res.items()\n",
-    "}).T"
+    "res = {name: backtest_multi(exps[name]) for name in tqdm(exps)}\n",
+    "report = pd.DataFrame({k: v[0] for k, v in res.items()}).T"
   ]
  },
  {
@@ -597,18 +619,22 @@
    }
   ],
   "source": [
-    "a = pd.read_pickle('output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb0.0_head4_hs64_bs512_do0.1_lr0.0005_seed3000/pred.pkl')\n",
-    "b = pd.read_pickle('output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb2.0_head4_hs64_bs512_do0.1_lr0.0005_seed3000/pred.pkl')\n",
+    "a = pd.read_pickle(\n",
+    "    \"output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb0.0_head4_hs64_bs512_do0.1_lr0.0005_seed3000/pred.pkl\"\n",
+    ")\n",
+    "b = pd.read_pickle(\n",
+    "    \"output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb2.0_head4_hs64_bs512_do0.1_lr0.0005_seed3000/pred.pkl\"\n",
+    ")\n",
    "a = a.iloc[:, -3:]\n",
    "b = b.iloc[:, -3:]\n",
    "b = np.eye(3)[b.values.argmax(axis=1)]\n",
    "a = np.eye(3)[a.values.argmax(axis=1)]\n",
    "\n",
-    "res = pd.DataFrame({\n",
-    "    'with OT': b.sum(axis=0) / b.sum(),\n",
-    "    'without OT': a.sum(axis=0)/ a.sum()  \n",
-    "},index=[r'$\\theta_1$',r'$\\theta_2$',r'$\\theta_3$'])\n",
-    "res.plot.bar(rot=30, figsize=(5, 4), color=['b', 'g'])\n",
+    "res = pd.DataFrame(\n",
+    "    {\"with OT\": b.sum(axis=0) / b.sum(), \"without OT\": a.sum(axis=0) / a.sum()},\n",
+    "    index=[r\"$\\theta_1$\", r\"$\\theta_2$\", r\"$\\theta_3$\"],\n",
+    ")\n",
+    "res.plot.bar(rot=30, figsize=(5, 4), color=[\"b\", \"g\"])\n",
    "del a, b"
   ]
  },
@@ -633,11 +659,19 @@
   "outputs": [],
   "source": [
    "exps = {\n",
-    "    'K=1': glob.glob('output/search/LSTM_Attn/hs256_bs1024_do0.1_lr0.0002_seed*/info.json'),\n",
-    "    'K=3': glob.glob('output/search/finetune/LSTM_Attn_tra/K3_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json'),\n",
-    "    'K=5': glob.glob('output/search/finetune/LSTM_Attn_tra/K5_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json'),\n",
-    "    'K=10': glob.glob('output/search/finetune/LSTM_Attn_tra/K10_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json'),\n",
-    "    'K=20': glob.glob('output/search/finetune/LSTM_Attn_tra/K20_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json')\n",
+    "    \"K=1\": glob.glob(\"output/search/LSTM_Attn/hs256_bs1024_do0.1_lr0.0002_seed*/info.json\"),\n",
+    "    \"K=3\": glob.glob(\n",
+    "        \"output/search/finetune/LSTM_Attn_tra/K3_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json\"\n",
+    "    ),\n",
+    "    \"K=5\": glob.glob(\n",
+    "        \"output/search/finetune/LSTM_Attn_tra/K5_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json\"\n",
+    "    ),\n",
+    "    \"K=10\": glob.glob(\n",
+    "        \"output/search/finetune/LSTM_Attn_tra/K10_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json\"\n",
+    "    ),\n",
+    "    \"K=20\": glob.glob(\n",
+    "        \"output/search/finetune/LSTM_Attn_tra/K20_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json\"\n",
+    "    ),\n",
    "}"
   ]
  },
@@ -649,16 +683,11 @@
   "source": [
    "report = dict()\n",
    "for k, v in exps.items():\n",
-    "    \n",
    "    tmp = dict()\n",
    "    for fname in v:\n",
    "        with open(fname) as f:\n",
    "            info = json.load(f)\n",
-    "        tmp[fname] = (\n",
-    "        {\n",
-    "            \"IC\":info[\"metric\"][\"IC\"],\n",
-    "            \"MSE\":info[\"metric\"][\"MSE\"]\n",
-    "        })\n",
+    "        tmp[fname] = {\"IC\": info[\"metric\"][\"IC\"], \"MSE\": info[\"metric\"][\"MSE\"]}\n",
    "    tmp = pd.DataFrame(tmp).T\n",
    "    report[k] = tmp.mean()\n",
    "report = pd.DataFrame(report).T"
@@ -681,13 +710,14 @@
    }
   ],
   "source": [
-    "fig, axes = plt.subplots(1, 2, figsize=(6,3)); axes = axes.flatten()\n",
-    "report['IC'].plot.bar(rot=30, ax=axes[0])\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(6, 3))\n",
+    "axes = axes.flatten()\n",
+    "report[\"IC\"].plot.bar(rot=30, ax=axes[0])\n",
    "axes[0].set_ylim(0.045, 0.062)\n",
-    "axes[0].set_title('IC performance')\n",
-    "report['MSE'].astype(float).plot.bar(rot=30, ax=axes[1], color='green')\n",
+    "axes[0].set_title(\"IC performance\")\n",
+    "report[\"MSE\"].astype(float).plot.bar(rot=30, ax=axes[1], color=\"green\")\n",
    "axes[1].set_ylim(0.155, 0.1585)\n",
-    "axes[1].set_title('MSE performance')\n",
+    "axes[1].set_title(\"MSE performance\")\n",
    "plt.tight_layout()\n",
    "# plt.savefig('sensitivity.pdf')"
   ]
--- a/examples/rl/simple_example.ipynb
+++ b/examples/rl/simple_example.ipynb
@@ -41,6 +41,7 @@
    "\n",
    "State = namedtuple(\"State\", [\"value\", \"last_action\"])\n",
    "\n",
+    "\n",
    "class SimpleSimulator(Simulator[float, State, float]):\n",
    "    def __init__(self, initial: float, nsteps: int, **kwargs: Any) -> None:\n",
    "        super().__init__(initial)\n",
@@ -92,6 +93,7 @@
    "from gym import spaces\n",
    "from qlib.rl.interpreter import StateInterpreter\n",
    "\n",
+    "\n",
    "class SimpleStateInterpreter(StateInterpreter[Tuple[float, float], np.ndarray]):\n",
    "    def interpret(self, state: State) -> np.ndarray:\n",
    "        # Convert state.value to a 1D Numpy array\n",
@@ -101,7 +103,8 @@
    "    @property\n",
    "    def observation_space(self) -> spaces.Box:\n",
    "        return spaces.Box(0, np.inf, shape=(1,), dtype=np.float32)\n",
-    "    \n",
+    "\n",
+    "\n",
    "state_interpreter = SimpleStateInterpreter()"
   ]
  },
@@ -120,6 +123,7 @@
   "source": [
    "from qlib.rl.interpreter import ActionInterpreter\n",
    "\n",
+    "\n",
    "class SimpleActionInterpreter(ActionInterpreter[State, int, float]):\n",
    "    def __init__(self, n_value: int) -> None:\n",
    "        self.n_value = n_value\n",
@@ -132,7 +136,8 @@
    "        assert 0 <= action <= self.n_value\n",
    "        # simulator_state.value is used as the denominator\n",
    "        return simulator_state.value * (action / self.n_value)\n",
-    "    \n",
+    "\n",
+    "\n",
    "action_interpreter = SimpleActionInterpreter(n_value=10)"
   ]
  },
@@ -151,12 +156,14 @@
   "source": [
    "from qlib.rl.reward import Reward\n",
    "\n",
+    "\n",
    "class SimpleReward(Reward[State]):\n",
    "    def reward(self, simulator_state: State) -> float:\n",
    "        # Use last_action to calculate reward. This is why it should be in the state.\n",
    "        rew = simulator_state.last_action / simulator_state.value\n",
    "        return rew\n",
-    "    \n",
+    "\n",
+    "\n",
    "reward = SimpleReward()"
   ]
  },
@@ -180,6 +187,7 @@
    "from torch import nn\n",
    "from qlib.rl.order_execution import PPO\n",
    "\n",
+    "\n",
    "class SimpleFullyConnect(nn.Module):\n",
    "    def __init__(self, dims: List[int]) -> None:\n",
    "        super().__init__()\n",
@@ -195,7 +203,8 @@
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        return self.fc(x)\n",
-    "    \n",
+    "\n",
+    "\n",
    "policy = PPO(\n",
    "    network=SimpleFullyConnect(dims=[16, 8]),\n",
    "    obs_space=state_interpreter.observation_space,\n",
@@ -221,6 +230,7 @@
   "source": [
    "from torch.utils.data import Dataset\n",
    "\n",
+    "\n",
    "class SimpleDataset(Dataset):\n",
    "    def __init__(self, positions: List[float]) -> None:\n",
    "        self.positions = positions\n",
@@ -230,7 +240,8 @@
    "\n",
    "    def __getitem__(self, index: int) -> float:\n",
    "        return self.positions[index]\n",
-    "    \n",
+    "\n",
+    "\n",
    "dataset = SimpleDataset(positions=[10.0, 50.0, 100.0])"
   ]
  },
@@ -265,11 +276,13 @@
    "trainer_kwargs = {\n",
    "    \"max_iters\": 10,\n",
    "    \"finite_env_type\": \"dummy\",\n",
-    "    \"callbacks\": [Checkpoint(\n",
-    "        dirpath=Path(\"./checkpoints\"),\n",
-    "        every_n_iters=1,\n",
-    "        save_latest=\"copy\",\n",
-    "    )],\n",
+    "    \"callbacks\": [\n",
+    "        Checkpoint(\n",
+    "            dirpath=Path(\"./checkpoints\"),\n",
+    "            every_n_iters=1,\n",
+    "            save_latest=\"copy\",\n",
+    "        )\n",
+    "    ],\n",
    "}\n",
    "vessel_kwargs = {\n",
    "    \"update_kwargs\": {\"batch_size\": 16, \"repeat\": 5},\n",
--- a/examples/tutorial/detailed_workflow.ipynb
+++ b/examples/tutorial/detailed_workflow.ipynb
@@ -88,6 +88,7 @@
   "outputs": [],
   "source": [
    "from qlib.tests.data import GetData\n",
+    "\n",
    "GetData().qlib_data(exists_skip=True)"
   ]
  },
@@ -99,6 +100,7 @@
   "outputs": [],
   "source": [
    "import qlib\n",
+    "\n",
    "qlib.init()"
   ]
  },
@@ -134,7 +136,8 @@
   "outputs": [],
   "source": [
    "from qlib.data import D\n",
-    "D.calendar(start_time='2010-01-01', end_time='2017-12-31', freq='day')[:2]  # calendar data"
+    "\n",
+    "print(D.calendar(start_time=\"2010-01-01\", end_time=\"2017-12-31\", freq=\"day\")[:2])  # calendar data"
   ]
  },
  {
@@ -152,7 +155,12 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "df = D.features(['SH601216'], ['$open', '$high', '$low', '$close', '$factor'], start_time='2020-05-01', end_time='2020-05-31')   "
+    "df = D.features(\n",
+    "    [\"SH601216\"],\n",
+    "    [\"$open\", \"$high\", \"$low\", \"$close\", \"$factor\"],\n",
+    "    start_time=\"2020-05-01\",\n",
+    "    end_time=\"2020-05-31\",\n",
+    ")"
   ]
  },
  {
@@ -163,11 +171,18 @@
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
-    "fig = go.Figure(data=[go.Candlestick(x=df.index.get_level_values(\"datetime\"),\n",
-    "                open=df['$open'],\n",
-    "                high=df['$high'],\n",
-    "                low=df['$low'],\n",
-    "                close=df['$close'])])\n",
+    "\n",
+    "fig = go.Figure(\n",
+    "    data=[\n",
+    "        go.Candlestick(\n",
+    "            x=df.index.get_level_values(\"datetime\"),\n",
+    "            open=df[\"$open\"],\n",
+    "            high=df[\"$high\"],\n",
+    "            low=df[\"$low\"],\n",
+    "            close=df[\"$close\"],\n",
+    "        )\n",
+    "    ]\n",
+    ")\n",
    "fig.show()"
   ]
  },
@@ -197,11 +212,18 @@
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
-    "fig = go.Figure(data=[go.Candlestick(x=df.index.get_level_values(\"datetime\"),\n",
-    "                open=df['$open'] / df['$factor'],\n",
-    "                high=df['$high'] / df['$factor'],\n",
-    "                low=df['$low'] / df['$factor'],\n",
-    "                close=df['$close'] / df['$factor'])])\n",
+    "\n",
+    "fig = go.Figure(\n",
+    "    data=[\n",
+    "        go.Candlestick(\n",
+    "            x=df.index.get_level_values(\"datetime\"),\n",
+    "            open=df[\"$open\"] / df[\"$factor\"],\n",
+    "            high=df[\"$high\"] / df[\"$factor\"],\n",
+    "            low=df[\"$low\"] / df[\"$factor\"],\n",
+    "            close=df[\"$close\"] / df[\"$factor\"],\n",
+    "        )\n",
+    "    ]\n",
+    ")\n",
    "fig.show()"
   ]
  },
@@ -240,7 +262,7 @@
   "outputs": [],
   "source": [
    "# dynamic universe\n",
-    "universe = D.list_instruments(D.instruments('csi100'),  start_time='2010-01-01', end_time='2020-12-31')\n",
+    "universe = D.list_instruments(D.instruments(\"csi100\"), start_time=\"2010-01-01\", end_time=\"2020-12-31\")\n",
    "pprint(universe)"
   ]
  },
@@ -271,8 +293,8 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "df = D.features(D.instruments('csi100'), ['$close'], start_time='2010-01-01', end_time='2020-12-31')   \n",
-    "df.groupby('datetime').size().plot()"
+    "df = D.features(D.instruments(\"csi100\"), [\"$close\"], start_time=\"2010-01-01\", end_time=\"2020-12-31\")\n",
+    "df.groupby(\"datetime\").size().plot()"
   ]
  },
  {
@@ -313,8 +335,7 @@
    "    !cd ../../scripts/data_collector/pit/ && pip install -r requirements.txt\n",
    "    !cd ../../scripts/data_collector/pit/ && python collector.py download_data --source_dir ~/.qlib/stock_data/source/pit --start 2000-01-01 --end 2020-01-01 --interval quarterly --symbol_regex \"^(600519|000725).*\"\n",
    "    !cd ../../scripts/data_collector/pit/ && python collector.py normalize_data --interval quarterly --source_dir ~/.qlib/stock_data/source/pit --normalize_dir ~/.qlib/stock_data/source/pit_normalized\n",
-    "    !cd ../../scripts/ && python dump_pit.py dump --csv_path ~/.qlib/stock_data/source/pit_normalized --qlib_dir ~/.qlib/qlib_data/cn_data --interval quarterly\n",
-    "    pass"
+    "    !cd ../../scripts/ && python dump_pit.py dump --csv_path ~/.qlib/stock_data/source/pit_normalized --qlib_dir ~/.qlib/qlib_data/cn_data --interval quarterly"
   ]
  },
  {
@@ -338,7 +359,13 @@
   "outputs": [],
   "source": [
    "instruments = [\"sh600519\"]\n",
-    "data = D.features(instruments, ['P($$roewa_q)'], start_time=\"2019-01-01\", end_time=\"2019-07-19\", freq=\"day\")"
+    "data = D.features(\n",
+    "    instruments,\n",
+    "    [\"P($$roewa_q)\"],\n",
+    "    start_time=\"2019-01-01\",\n",
+    "    end_time=\"2019-07-19\",\n",
+    "    freq=\"day\",\n",
+    ")"
   ]
  },
  {
@@ -366,7 +393,10 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "D.features([\"sh600519\"], ['(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close'])"
+    "D.features(\n",
+    "    [\"sh600519\"],\n",
+    "    [\"(EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$close\"],\n",
+    ")"
   ]
  },
  {
@@ -418,7 +448,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "qdl = QlibDataLoader(config=(['$close / Ref($close, 10)'], ['RET10']))"
+    "qdl = QlibDataLoader(config=([\"$close / Ref($close, 10)\"], [\"RET10\"]))"
   ]
  },
  {
@@ -428,7 +458,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "qdl.load(instruments=['sh600519'], start_time='20190101', end_time='20191231')"
+    "qdl.load(instruments=[\"sh600519\"], start_time=\"20190101\", end_time=\"20191231\")"
   ]
  },
  {
@@ -456,7 +486,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "df = qdl.load(instruments=['sh600519'], start_time='20190101', end_time='20191231')"
+    "df = qdl.load(instruments=[\"sh600519\"], start_time=\"20190101\", end_time=\"20191231\")"
   ]
  },
  {
@@ -476,7 +506,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "df.plot(kind='hist')"
+    "df.plot(kind=\"hist\")"
   ]
  },
  {
@@ -508,9 +538,16 @@
   "source": [
    "# NOTE: normally, the training & validation time range will be  `fit_start_time` ， `fit_end_time`\n",
    "# however，all the components are decomposed, so the training & validation time range is unknown when preprocessing.\n",
-    "dh = DataHandlerLP(instruments=['sh600519'], start_time='20170101', end_time='20191231',\n",
-    "             infer_processors=[ZScoreNorm(fit_start_time='20170101', fit_end_time='20181231'), Fillna()],\n",
-    "             data_loader=qdl)"
+    "dh = DataHandlerLP(\n",
+    "    instruments=[\"sh600519\"],\n",
+    "    start_time=\"20170101\",\n",
+    "    end_time=\"20191231\",\n",
+    "    infer_processors=[\n",
+    "        ZScoreNorm(fit_start_time=\"20170101\", fit_end_time=\"20181231\"),\n",
+    "        Fillna(),\n",
+    "    ],\n",
+    "    data_loader=qdl,\n",
+    ")"
   ]
  },
  {
@@ -550,7 +587,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "df.plot(kind='hist')"
+    "df.plot(kind=\"hist\")"
   ]
  },
  {
@@ -586,7 +623,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "ds = DatasetH(dh, segments={\"train\": ('20180101', '20181231'), \"valid\": ('20190101', '20191231')})"
+    "ds = DatasetH(dh, segments={\"train\": (\"20180101\", \"20181231\"), \"valid\": (\"20190101\", \"20191231\")})"
   ]
  },
  {
@@ -596,7 +633,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "ds.prepare('train')"
+    "ds.prepare(\"train\")"
   ]
  },
  {
@@ -606,7 +643,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "ds.prepare('valid')"
+    "ds.prepare(\"valid\")"
   ]
  },
  {
@@ -628,8 +665,12 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "ds = TSDatasetH(step_len=10, handler=dh, segments={\"train\": ('20180101', '20181231'), \"valid\": ('20190101', '20191231')})\n",
-    "train_sampler = ds.prepare('train')"
+    "ds = TSDatasetH(\n",
+    "    step_len=10,\n",
+    "    handler=dh,\n",
+    "    segments={\"train\": (\"20180101\", \"20181231\"), \"valid\": (\"20190101\", \"20191231\")},\n",
+    ")\n",
+    "train_sampler = ds.prepare(\"train\")"
   ]
  },
  {
@@ -649,7 +690,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "train_sampler[0] # Retrieving the first example"
+    "train_sampler[0]  # Retrieving the first example"
   ]
  },
  {
@@ -659,7 +700,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "train_sampler['2018-01-08', 'sh600519']  # get the time series by <'timestamp', 'instrument_id'> index"
+    "train_sampler[\"2018-01-08\", \"sh600519\"]  # get the time series by <'timestamp', 'instrument_id'> index"
   ]
  },
  {
@@ -682,11 +723,11 @@
   "outputs": [],
   "source": [
    "handler_kwargs = {\n",
-    "        \"start_time\": \"2008-01-01\",\n",
-    "        \"end_time\": \"2020-08-01\",\n",
-    "        \"fit_start_time\": \"2008-01-01\",\n",
-    "        \"fit_end_time\": \"2014-12-31\",\n",
-    "        \"instruments\": MARKET,\n",
+    "    \"start_time\": \"2008-01-01\",\n",
+    "    \"end_time\": \"2020-08-01\",\n",
+    "    \"fit_start_time\": \"2008-01-01\",\n",
+    "    \"fit_end_time\": \"2014-12-31\",\n",
+    "    \"instruments\": MARKET,\n",
    "}\n",
    "handler_conf = {\n",
    "    \"class\": \"Alpha158\",\n",
@@ -735,6 +776,7 @@
   "outputs": [],
   "source": [
    "from qlib.contrib.data.handler import Alpha158\n",
+    "\n",
    "hd = Alpha158(**handler_kwargs)"
   ]
  },
@@ -826,7 +868,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "hd.process_type # appending type"
+    "hd.process_type  # appending type"
   ]
  },
  {
@@ -857,16 +899,16 @@
   "outputs": [],
   "source": [
    "dataset_conf = {\n",
-    "        \"class\": \"DatasetH\",\n",
-    "        \"module_path\": \"qlib.data.dataset\",\n",
-    "        \"kwargs\": {\n",
-    "            \"handler\": hd,\n",
-    "            \"segments\": {\n",
-    "                \"train\": (\"2008-01-01\", \"2014-12-31\"),\n",
-    "                \"valid\": (\"2015-01-01\", \"2016-12-31\"),\n",
-    "                \"test\": (\"2017-01-01\", \"2020-08-01\"),\n",
-    "            },\n",
+    "    \"class\": \"DatasetH\",\n",
+    "    \"module_path\": \"qlib.data.dataset\",\n",
+    "    \"kwargs\": {\n",
+    "        \"handler\": hd,\n",
+    "        \"segments\": {\n",
+    "            \"train\": (\"2008-01-01\", \"2014-12-31\"),\n",
+    "            \"valid\": (\"2015-01-01\", \"2016-12-31\"),\n",
+    "            \"test\": (\"2017-01-01\", \"2020-08-01\"),\n",
    "        },\n",
+    "    },\n",
    "}"
   ]
  },
@@ -908,7 +950,8 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "model = init_instance_by_config({\n",
+    "model = init_instance_by_config(\n",
+    "    {\n",
    "        \"class\": \"LGBModel\",\n",
    "        \"module_path\": \"qlib.contrib.model.gbdt\",\n",
    "        \"kwargs\": {\n",
@@ -922,7 +965,8 @@
    "            \"num_leaves\": 210,\n",
    "            \"num_threads\": 20,\n",
    "        },\n",
-    "})"
+    "    }\n",
+    ")"
   ]
  },
  {
@@ -938,7 +982,7 @@
    "    R.save_objects(trained_model=model)\n",
    "\n",
    "    rec = R.get_recorder()\n",
-    "    rid = rec.id # save the record id\n",
+    "    rid = rec.id  # save the record id\n",
    "\n",
    "    # Inference and saving signal\n",
    "    sr = SignalRecord(model, dataset, rec)\n",
@@ -1001,12 +1045,11 @@
    "\n",
    "# backtest and analysis\n",
    "with R.start(experiment_name=EXP_NAME, recorder_id=rid, resume=True):\n",
-    "\n",
    "    # signal-based analysis\n",
    "    rec = R.get_recorder()\n",
    "    sar = SigAnaRecord(rec)\n",
    "    sar.generate()\n",
-    "    \n",
+    "\n",
    "    #  portfolio-based analysis: backtest\n",
    "    par = PortAnaRecord(rec, port_analysis_config, \"day\")\n",
    "    par.generate()"
@@ -1137,7 +1180,7 @@
   "outputs": [],
   "source": [
    "label_df = dataset.prepare(\"test\", col_set=\"label\")\n",
-    "label_df.columns = ['label']"
+    "label_df.columns = [\"label\"]"
   ]
  },
  {
--- a/examples/workflow_by_code.ipynb
+++ b/examples/workflow_by_code.ipynb
@@ -38,7 +38,7 @@
    "    # install qlib\n",
    "    ! pip install --upgrade numpy\n",
    "    ! pip install pyqlib\n",
-    "    if 'google.colab' in sys.modules:\n",
+    "    if \"google.colab\" in sys.modules:\n",
    "        # The Google colab environment is a little outdated. We have to downgrade the pyyaml to make it compatible with other packages\n",
    "        ! pip install pyyaml==5.4.1\n",
    "    # reload\n",
@@ -50,7 +50,8 @@
    "    scripts_dir = Path(\"~/tmp/qlib_code/scripts\").expanduser().resolve()\n",
    "    scripts_dir.mkdir(parents=True, exist_ok=True)\n",
    "    import requests\n",
-    "    with requests.get(\"https://raw.githubusercontent.com/microsoft/qlib/main/scripts/get_data.py\") as resp:\n",
+    "\n",
+    "    with requests.get(\"https://raw.githubusercontent.com/microsoft/qlib/main/scripts/get_data.py\", timeout=10) as resp:\n",
    "        with open(scripts_dir.joinpath(\"get_data.py\"), \"wb\") as fp:\n",
    "            fp.write(resp.content)"
   ]
@@ -61,14 +62,13 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "\n",
    "import qlib\n",
    "import pandas as pd\n",
    "from qlib.constant import REG_CN\n",
    "from qlib.utils import exists_qlib_data, init_instance_by_config\n",
    "from qlib.workflow import R\n",
    "from qlib.workflow.record_temp import SignalRecord, PortAnaRecord\n",
-    "from qlib.utils import flatten_dict\n"
+    "from qlib.utils import flatten_dict"
   ]
  },
  {
@@ -86,6 +86,7 @@
    "    print(f\"Qlib data is not found in {provider_uri}\")\n",
    "    sys.path.append(str(scripts_dir))\n",
    "    from get_data import GetData\n",
+    "\n",
    "    GetData().qlib_data(target_dir=provider_uri, region=REG_CN)\n",
    "qlib.init(provider_uri=provider_uri, region=REG_CN)"
   ]
@@ -169,7 +170,7 @@
    "    R.log_params(**flatten_dict(task))\n",
    "    model.fit(dataset)\n",
    "    R.save_objects(trained_model=model)\n",
-    "    rid = R.get_recorder().id\n"
+    "    rid = R.get_recorder().id"
   ]
  },
  {
@@ -238,7 +239,7 @@
    "\n",
    "    # backtest & analysis\n",
    "    par = PortAnaRecord(recorder, port_analysis_config, \"day\")\n",
-    "    par.generate()\n"
+    "    par.generate()"
   ]
  },
  {
@@ -256,6 +257,7 @@
   "source": [
    "from qlib.contrib.report import analysis_model, analysis_position\n",
    "from qlib.data import D\n",
+    "\n",
    "recorder = R.get_recorder(recorder_id=ba_rid, experiment_name=\"backtest_analysis\")\n",
    "print(recorder)\n",
    "pred_df = recorder.load_object(\"pred.pkl\")\n",
@@ -317,7 +319,7 @@
   "outputs": [],
   "source": [
    "label_df = dataset.prepare(\"test\", col_set=\"label\")\n",
-    "label_df.columns = ['label']"
+    "label_df.columns = [\"label\"]"
   ]
  },
  {
--- a/setup.py
+++ b/setup.py
@@ -146,6 +146,9 @@ setup(
            # References: https://github.com/python/typeshed/issues/8799
            "mypy<0.981",
            "flake8",
+            "nbqa",
+            "jupyter",
+            "nbconvert",
            # The 5.0.0 version of importlib-metadata removed the deprecated endpoint,
            # which prevented flake8 from working properly, so we restricted the version of importlib-metadata.
            # To help ensure the dependencies of flake8 https://github.com/python/importlib_metadata/issues/406