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Add ipynb format check (#1439)

Browse Source 
* 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
This commit is contained in:
Cadenza-Li
2023-02-21 09:23:22 +08:00
committed by GitHub
parent 5eb5ac1f1f
commit 76f2fb1a1a
6 changed files with 275 additions and 173 deletions
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11
.github/workflows/test_qlib_from_source.yml vendored
View File

@@ -122,6 +122,11 @@ jobs:
mypy qlib --install-types --non-interactive || true mypy qlib --install-types --non-interactive || true
mypy qlib --verbose 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 - name: Test data downloads
run: | run: |
python scripts/get_data.py qlib_data --name qlib_data_simple --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn python scripts/get_data.py qlib_data --name qlib_data_simple --target_dir ~/.qlib/qlib_data/cn_data --interval 1d --region cn
@@ -139,6 +144,12 @@ jobs:
brew unlink libomp brew unlink libomp
brew install libomp.rb 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) - name: Test workflow by config (install from source)
run: | run: |
python -m pip install numba python -m pip install numba

232
examples/benchmarks/TRA/Reports.ipynb
View File

@@ -25,59 +25,65 @@
"import seaborn as sns\n", "import seaborn as sns\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import matplotlib\n", "import matplotlib\n",
"sns.set(style='white')\n", "\n",
"matplotlib.rcParams['pdf.fonttype'] = 42\n", "sns.set(style=\"white\")\n",
"matplotlib.rcParams['ps.fonttype'] = 42\n", "matplotlib.rcParams[\"pdf.fonttype\"] = 42\n",
"matplotlib.rcParams[\"ps.fonttype\"] = 42\n",
"\n", "\n",
"from tqdm.auto import tqdm\n", "from tqdm.auto import tqdm\n",
"from joblib import Parallel, delayed\n", "from joblib import Parallel, delayed\n",
"\n", "\n",
"\n",
"def func(x, N=80):\n", "def func(x, N=80):\n",
" ret = x.ret.copy()\n", " ret = x.ret.copy()\n",
" x = x.rank(pct=True)\n", " x = x.rank(pct=True)\n",
" x['ret'] = ret\n", " x[\"ret\"] = ret\n",
" diff = x.score.sub(x.label)\n", " diff = x.score.sub(x.label)\n",
" r = x.nlargest(N, columns='score').ret.mean()\n", " r = x.nlargest(N, columns=\"score\").ret.mean()\n",
" r -= x.nsmallest(N, columns='score').ret.mean()\n", " r -= x.nsmallest(N, columns=\"score\").ret.mean()\n",
" return pd.Series({\n", " return pd.Series(\n",
" 'MSE': diff.pow(2).mean(), \n", " {\n",
" 'MAE': diff.abs().mean(), \n", " \"MSE\": diff.pow(2).mean(),\n",
" 'IC': x.score.corr(x.label),\n", " \"MAE\": diff.abs().mean(),\n",
" 'R': r\n", " \"IC\": x.score.corr(x.label),\n",
" })\n", " \"R\": r,\n",
" \n", " }\n",
" )\n",
"\n",
"\n",
"ret = pd.read_pickle(\"data/ret.pkl\").clip(-0.1, 0.1)\n", "ret = pd.read_pickle(\"data/ret.pkl\").clip(-0.1, 0.1)\n",
"\n",
"\n",
"def backtest(fname, **kwargs):\n", "def backtest(fname, **kwargs):\n",
" pred = pd.read_pickle(fname).loc['2018-09-21':'2020-06-30'] # test period\n", " pred = pd.read_pickle(fname).loc[\"2018-09-21\":\"2020-06-30\"] # test period\n",
" pred['ret'] = ret\n", " pred[\"ret\"] = ret\n",
" dates = pred.index.unique(level=0)\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 = Parallel(n_jobs=-1)(delayed(func)(pred.loc[d], **kwargs) for d in dates)\n",
" res = {\n", " res = {dates[i]: res[i] for i in range(len(dates))}\n",
" dates[i]: res[i]\n",
" for i in range(len(dates))\n",
" }\n",
" res = pd.DataFrame(res).T\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.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", " r = r.reindex(pd.date_range(r.index[0], r.index[-1])).fillna(0) # paper use 365 days\n",
" return {\n", " return {\n",
" 'MSE': res['MSE'].mean(),\n", " \"MSE\": res[\"MSE\"].mean(),\n",
" 'MAE': res['MAE'].mean(),\n", " \"MAE\": res[\"MAE\"].mean(),\n",
" 'IC': res['IC'].mean(),\n", " \"IC\": res[\"IC\"].mean(),\n",
" 'ICIR': res['IC'].mean()/res['IC'].std(),\n", " \"ICIR\": res[\"IC\"].mean() / res[\"IC\"].std(),\n",
" 'AR': r.mean()*365,\n", " \"AR\": r.mean() * 365,\n",
" 'AV': r.std()*365**0.5,\n", " \"AV\": r.std() * 365**0.5,\n",
" 'SR': r.mean()/r.std()*365**0.5,\n", " \"SR\": r.mean() / r.std() * 365**0.5,\n",
" 'MDD': (r.cumsum().cummax() - r.cumsum()).max()\n", " \"MDD\": (r.cumsum().cummax() - r.cumsum()).max(),\n",
" }, r\n", " }, r\n",
"\n", "\n",
"\n",
"def fmt(x, p=3, scale=1, std=False):\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", " string = _fmt.format((x.mean() if not isinstance(x, (float, np.floating)) else x) * scale)\n",
" if std and len(x) > 1:\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", " return string\n",
"\n", "\n",
"\n",
"def backtest_multi(files, **kwargs):\n", "def backtest_multi(files, **kwargs):\n",
" res = []\n", " res = []\n",
" pnl = []\n", " pnl = []\n",
@@ -88,14 +94,14 @@
" res = pd.DataFrame(res)\n", " res = pd.DataFrame(res)\n",
" pnl = pd.concat(pnl, axis=1)\n", " pnl = pd.concat(pnl, axis=1)\n",
" return {\n", " return {\n",
" 'MSE': fmt(res['MSE'], std=True),\n", " \"MSE\": fmt(res[\"MSE\"], std=True),\n",
" 'MAE': fmt(res['MAE'], std=True),\n", " \"MAE\": fmt(res[\"MAE\"], std=True),\n",
" 'IC': fmt(res['IC']),\n", " \"IC\": fmt(res[\"IC\"]),\n",
" 'ICIR': fmt(res['ICIR']),\n", " \"ICIR\": fmt(res[\"ICIR\"]),\n",
" 'AR': fmt(res['AR'], scale=100, p=1)+'%',\n", " \"AR\": fmt(res[\"AR\"], scale=100, p=1) + \"%\",\n",
" 'VR': fmt(res['AV'], scale=100, p=1)+'%',\n", " \"VR\": fmt(res[\"AV\"], scale=100, p=1) + \"%\",\n",
" 'SR': fmt(res['SR']),\n", " \"SR\": fmt(res[\"SR\"]),\n",
" 'MDD': fmt(res['MDD'], scale=100, p=1)+'%'\n", " \"MDD\": fmt(res[\"MDD\"], scale=100, p=1) + \"%\",\n",
" }, pnl" " }, pnl"
] ]
}, },
@@ -124,16 +130,20 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"exps = {\n", "exps = {\n",
" 'Linear': ['output/Linear/pred.pkl'],\n", " \"Linear\": [\"output/Linear/pred.pkl\"],\n",
" 'LightGBM': ['output/GBDT/lr0.05_leaves128/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", " \"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", " \"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", " \"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", " \"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", " \"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", " \"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", " \"ALSTM+TRA(Ours)\": glob.glob(\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", " \"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": [ "source": [
"res = {\n", "res = {name: backtest_multi(exps[name]) for name in tqdm(exps)}\n",
" name: backtest_multi(exps[name])\n", "report = pd.DataFrame({k: v[0] for k, v in res.items()}).T"
" for name in tqdm(exps)\n",
"}\n",
"report = pd.DataFrame({\n",
" k: v[0]\n",
" for k, v in res.items()\n",
"}).T"
] ]
}, },
{ {
@@ -385,24 +389,40 @@
} }
], ],
"source": [ "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", "df = pd.read_pickle(\n",
"code = 'SH600157'\n", " \"output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb0.0_head4_hs64_bs512_do0.1_lr0.0005_seed1000/pred.pkl\"\n",
"date = '2018-09-28'\n", ")\n",
"code = \"SH600157\"\n",
"date = \"2018-09-28\"\n",
"lookbackperiod = 50\n", "lookbackperiod = 50\n",
"\n", "\n",
"prob = df.iloc[:, -3:].loc(axis=0)[:, code].reset_index(level=1, drop=True).loc[date:].iloc[:lookbackperiod]\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", "pred = (\n",
"e_all = pred.iloc[:,:-1].sub(pred.iloc[:,-1], axis=0).pow(2)\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 = 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", "prob = pd.Series(np.argmax(prob.values, axis=1), index=prob.index).rolling(7).mean().round()\n",
"\n", "\n",
"fig, axes = plt.subplots(1, 2, figsize=(7, 3))\n", "fig, axes = plt.subplots(1, 2, figsize=(7, 3))\n",
"e_all.plot(ax=axes[0], xlabel='', rot=30)\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", "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", "plt.yticks(np.array([0, 1, 2]), e_all.columns.values)\n",
"axes[0].set_ylabel('Predictor Loss')\n", "axes[0].set_ylabel(\"Predictor Loss\")\n",
"axes[1].set_ylabel('Router Selection')\n", "axes[1].set_ylabel(\"Router Selection\")\n",
"plt.tight_layout()\n", "plt.tight_layout()\n",
"# plt.savefig('select.pdf', bbox_inches='tight')\n", "# plt.savefig('select.pdf', bbox_inches='tight')\n",
"plt.show()" "plt.show()"
@@ -428,10 +448,18 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"exps = {\n", "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", " \"Random\": glob.glob(\n",
" 'LR': glob.glob('output/search/LSTM_Attn_tra/K10_traHs16_traSrcLR_traLamb1.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl'),\n", " \"output/search/LSTM_Attn_tra/K10_traHs16_traSrcNONE_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", " ),\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", " \"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": [ "source": [
"res = {\n", "res = {name: backtest_multi(exps[name]) for name in tqdm(exps)}\n",
" name: backtest_multi(exps[name])\n", "report = pd.DataFrame({k: v[0] for k, v in res.items()}).T"
" for name in tqdm(exps)\n",
"}\n",
"report = pd.DataFrame({\n",
" k: v[0]\n",
" for k, v in res.items()\n",
"}).T"
] ]
}, },
{ {
@@ -597,18 +619,22 @@
} }
], ],
"source": [ "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", "a = pd.read_pickle(\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", " \"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", "a = a.iloc[:, -3:]\n",
"b = b.iloc[:, -3:]\n", "b = b.iloc[:, -3:]\n",
"b = np.eye(3)[b.values.argmax(axis=1)]\n", "b = np.eye(3)[b.values.argmax(axis=1)]\n",
"a = np.eye(3)[a.values.argmax(axis=1)]\n", "a = np.eye(3)[a.values.argmax(axis=1)]\n",
"\n", "\n",
"res = pd.DataFrame({\n", "res = pd.DataFrame(\n",
" 'with OT': b.sum(axis=0) / b.sum(),\n", " {\"with OT\": b.sum(axis=0) / b.sum(), \"without OT\": a.sum(axis=0) / a.sum()},\n",
" 'without OT': a.sum(axis=0)/ a.sum() \n", " index=[r\"$\\theta_1$\", r\"$\\theta_2$\", r\"$\\theta_3$\"],\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", "res.plot.bar(rot=30, figsize=(5, 4), color=[\"b\", \"g\"])\n",
"del a, b" "del a, b"
] ]
}, },
@@ -633,11 +659,19 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"exps = {\n", "exps = {\n",
" 'K=1': glob.glob('output/search/LSTM_Attn/hs256_bs1024_do0.1_lr0.0002_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('output/search/finetune/LSTM_Attn_tra/K3_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json'),\n", " \"K=3\": glob.glob(\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", " \"output/search/finetune/LSTM_Attn_tra/K3_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", " ),\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=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": [ "source": [
"report = dict()\n", "report = dict()\n",
"for k, v in exps.items():\n", "for k, v in exps.items():\n",
" \n",
" tmp = dict()\n", " tmp = dict()\n",
" for fname in v:\n", " for fname in v:\n",
" with open(fname) as f:\n", " with open(fname) as f:\n",
" info = json.load(f)\n", " info = json.load(f)\n",
" tmp[fname] = (\n", " tmp[fname] = {\"IC\": info[\"metric\"][\"IC\"], \"MSE\": info[\"metric\"][\"MSE\"]}\n",
" {\n",
" \"IC\":info[\"metric\"][\"IC\"],\n",
" \"MSE\":info[\"metric\"][\"MSE\"]\n",
" })\n",
" tmp = pd.DataFrame(tmp).T\n", " tmp = pd.DataFrame(tmp).T\n",
" report[k] = tmp.mean()\n", " report[k] = tmp.mean()\n",
"report = pd.DataFrame(report).T" "report = pd.DataFrame(report).T"
@@ -681,13 +710,14 @@
} }
], ],
"source": [ "source": [
"fig, axes = plt.subplots(1, 2, figsize=(6,3)); axes = axes.flatten()\n", "fig, axes = plt.subplots(1, 2, figsize=(6, 3))\n",
"report['IC'].plot.bar(rot=30, ax=axes[0])\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_ylim(0.045, 0.062)\n",
"axes[0].set_title('IC performance')\n", "axes[0].set_title(\"IC performance\")\n",
"report['MSE'].astype(float).plot.bar(rot=30, ax=axes[1], color='green')\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_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.tight_layout()\n",
"# plt.savefig('sensitivity.pdf')" "# plt.savefig('sensitivity.pdf')"
] ]

33
examples/rl/simple_example.ipynb
View File

@@ -41,6 +41,7 @@
"\n", "\n",
"State = namedtuple(\"State\", [\"value\", \"last_action\"])\n", "State = namedtuple(\"State\", [\"value\", \"last_action\"])\n",
"\n", "\n",
"\n",
"class SimpleSimulator(Simulator[float, State, float]):\n", "class SimpleSimulator(Simulator[float, State, float]):\n",
" def __init__(self, initial: float, nsteps: int, **kwargs: Any) -> None:\n", " def __init__(self, initial: float, nsteps: int, **kwargs: Any) -> None:\n",
" super().__init__(initial)\n", " super().__init__(initial)\n",
@@ -92,6 +93,7 @@
"from gym import spaces\n", "from gym import spaces\n",
"from qlib.rl.interpreter import StateInterpreter\n", "from qlib.rl.interpreter import StateInterpreter\n",
"\n", "\n",
"\n",
"class SimpleStateInterpreter(StateInterpreter[Tuple[float, float], np.ndarray]):\n", "class SimpleStateInterpreter(StateInterpreter[Tuple[float, float], np.ndarray]):\n",
" def interpret(self, state: State) -> np.ndarray:\n", " def interpret(self, state: State) -> np.ndarray:\n",
" # Convert state.value to a 1D Numpy array\n", " # Convert state.value to a 1D Numpy array\n",
@@ -101,7 +103,8 @@
" @property\n", " @property\n",
" def observation_space(self) -> spaces.Box:\n", " def observation_space(self) -> spaces.Box:\n",
" return spaces.Box(0, np.inf, shape=(1,), dtype=np.float32)\n", " return spaces.Box(0, np.inf, shape=(1,), dtype=np.float32)\n",
" \n", "\n",
"\n",
"state_interpreter = SimpleStateInterpreter()" "state_interpreter = SimpleStateInterpreter()"
] ]
}, },
@@ -120,6 +123,7 @@
"source": [ "source": [
"from qlib.rl.interpreter import ActionInterpreter\n", "from qlib.rl.interpreter import ActionInterpreter\n",
"\n", "\n",
"\n",
"class SimpleActionInterpreter(ActionInterpreter[State, int, float]):\n", "class SimpleActionInterpreter(ActionInterpreter[State, int, float]):\n",
" def __init__(self, n_value: int) -> None:\n", " def __init__(self, n_value: int) -> None:\n",
" self.n_value = n_value\n", " self.n_value = n_value\n",
@@ -132,7 +136,8 @@
" assert 0 <= action <= self.n_value\n", " assert 0 <= action <= self.n_value\n",
" # simulator_state.value is used as the denominator\n", " # simulator_state.value is used as the denominator\n",
" return simulator_state.value * (action / self.n_value)\n", " return simulator_state.value * (action / self.n_value)\n",
" \n", "\n",
"\n",
"action_interpreter = SimpleActionInterpreter(n_value=10)" "action_interpreter = SimpleActionInterpreter(n_value=10)"
] ]
}, },
@@ -151,12 +156,14 @@
"source": [ "source": [
"from qlib.rl.reward import Reward\n", "from qlib.rl.reward import Reward\n",
"\n", "\n",
"\n",
"class SimpleReward(Reward[State]):\n", "class SimpleReward(Reward[State]):\n",
" def reward(self, simulator_state: State) -> float:\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", " # 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", " rew = simulator_state.last_action / simulator_state.value\n",
" return rew\n", " return rew\n",
" \n", "\n",
"\n",
"reward = SimpleReward()" "reward = SimpleReward()"
] ]
}, },
@@ -180,6 +187,7 @@
"from torch import nn\n", "from torch import nn\n",
"from qlib.rl.order_execution import PPO\n", "from qlib.rl.order_execution import PPO\n",
"\n", "\n",
"\n",
"class SimpleFullyConnect(nn.Module):\n", "class SimpleFullyConnect(nn.Module):\n",
" def __init__(self, dims: List[int]) -> None:\n", " def __init__(self, dims: List[int]) -> None:\n",
" super().__init__()\n", " super().__init__()\n",
@@ -195,7 +203,8 @@
"\n", "\n",
" def forward(self, x: torch.Tensor) -> torch.Tensor:\n", " def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
" return self.fc(x)\n", " return self.fc(x)\n",
" \n", "\n",
"\n",
"policy = PPO(\n", "policy = PPO(\n",
" network=SimpleFullyConnect(dims=[16, 8]),\n", " network=SimpleFullyConnect(dims=[16, 8]),\n",
" obs_space=state_interpreter.observation_space,\n", " obs_space=state_interpreter.observation_space,\n",
@@ -221,6 +230,7 @@
"source": [ "source": [
"from torch.utils.data import Dataset\n", "from torch.utils.data import Dataset\n",
"\n", "\n",
"\n",
"class SimpleDataset(Dataset):\n", "class SimpleDataset(Dataset):\n",
" def __init__(self, positions: List[float]) -> None:\n", " def __init__(self, positions: List[float]) -> None:\n",
" self.positions = positions\n", " self.positions = positions\n",
@@ -230,7 +240,8 @@
"\n", "\n",
" def __getitem__(self, index: int) -> float:\n", " def __getitem__(self, index: int) -> float:\n",
" return self.positions[index]\n", " return self.positions[index]\n",
" \n", "\n",
"\n",
"dataset = SimpleDataset(positions=[10.0, 50.0, 100.0])" "dataset = SimpleDataset(positions=[10.0, 50.0, 100.0])"
] ]
}, },
@@ -265,11 +276,13 @@
"trainer_kwargs = {\n", "trainer_kwargs = {\n",
" \"max_iters\": 10,\n", " \"max_iters\": 10,\n",
" \"finite_env_type\": \"dummy\",\n", " \"finite_env_type\": \"dummy\",\n",
" \"callbacks\": [Checkpoint(\n", " \"callbacks\": [\n",
" dirpath=Path(\"./checkpoints\"),\n", " Checkpoint(\n",
" every_n_iters=1,\n", " dirpath=Path(\"./checkpoints\"),\n",
" save_latest=\"copy\",\n", " every_n_iters=1,\n",
" )],\n", " save_latest=\"copy\",\n",
" )\n",
" ],\n",
"}\n", "}\n",
"vessel_kwargs = {\n", "vessel_kwargs = {\n",
" \"update_kwargs\": {\"batch_size\": 16, \"repeat\": 5},\n", " \"update_kwargs\": {\"batch_size\": 16, \"repeat\": 5},\n",

153
examples/tutorial/detailed_workflow.ipynb
View File

@@ -88,6 +88,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"from qlib.tests.data import GetData\n", "from qlib.tests.data import GetData\n",
"\n",
"GetData().qlib_data(exists_skip=True)" "GetData().qlib_data(exists_skip=True)"
] ]
}, },
@@ -99,6 +100,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"import qlib\n", "import qlib\n",
"\n",
"qlib.init()" "qlib.init()"
] ]
}, },
@@ -134,7 +136,8 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"from qlib.data import D\n", "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": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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": [], "outputs": [],
"source": [ "source": [
"import plotly.graph_objects as go\n", "import plotly.graph_objects as go\n",
"fig = go.Figure(data=[go.Candlestick(x=df.index.get_level_values(\"datetime\"),\n", "\n",
" open=df['$open'],\n", "fig = go.Figure(\n",
" high=df['$high'],\n", " data=[\n",
" low=df['$low'],\n", " go.Candlestick(\n",
" close=df['$close'])])\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()" "fig.show()"
] ]
}, },
@@ -197,11 +212,18 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"import plotly.graph_objects as go\n", "import plotly.graph_objects as go\n",
"fig = go.Figure(data=[go.Candlestick(x=df.index.get_level_values(\"datetime\"),\n", "\n",
" open=df['$open'] / df['$factor'],\n", "fig = go.Figure(\n",
" high=df['$high'] / df['$factor'],\n", " data=[\n",
" low=df['$low'] / df['$factor'],\n", " go.Candlestick(\n",
" close=df['$close'] / df['$factor'])])\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()" "fig.show()"
] ]
}, },
@@ -240,7 +262,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"# dynamic universe\n", "# 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)" "pprint(universe)"
] ]
}, },
@@ -271,8 +293,8 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"df = D.features(D.instruments('csi100'), ['$close'], start_time='2010-01-01', end_time='2020-12-31') \n", "df = D.features(D.instruments(\"csi100\"), [\"$close\"], start_time=\"2010-01-01\", end_time=\"2020-12-31\")\n",
"df.groupby('datetime').size().plot()" "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/ && 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 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/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", " !cd ../../scripts/ && python dump_pit.py dump --csv_path ~/.qlib/stock_data/source/pit_normalized --qlib_dir ~/.qlib/qlib_data/cn_data --interval quarterly"
" pass"
] ]
}, },
{ {
@@ -338,7 +359,13 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"instruments = [\"sh600519\"]\n", "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": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"qdl = QlibDataLoader(config=(['$close / Ref($close, 10)'], ['RET10']))" "qdl = QlibDataLoader(config=([\"$close / Ref($close, 10)\"], [\"RET10\"]))"
] ]
}, },
{ {
@@ -428,7 +458,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"df.plot(kind='hist')" "df.plot(kind=\"hist\")"
] ]
}, },
{ {
@@ -508,9 +538,16 @@
"source": [ "source": [
"# NOTE: normally, the training & validation time range will be `fit_start_time` ， `fit_end_time`\n", "# 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", "# 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", "dh = DataHandlerLP(\n",
" infer_processors=[ZScoreNorm(fit_start_time='20170101', fit_end_time='20181231'), Fillna()],\n", " instruments=[\"sh600519\"],\n",
" data_loader=qdl)" " 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": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"df.plot(kind='hist')" "df.plot(kind=\"hist\")"
] ]
}, },
{ {
@@ -586,7 +623,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"ds.prepare('train')" "ds.prepare(\"train\")"
] ]
}, },
{ {
@@ -606,7 +643,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"ds.prepare('valid')" "ds.prepare(\"valid\")"
] ]
}, },
{ {
@@ -628,8 +665,12 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"ds = TSDatasetH(step_len=10, handler=dh, segments={\"train\": ('20180101', '20181231'), \"valid\": ('20190101', '20191231')})\n", "ds = TSDatasetH(\n",
"train_sampler = ds.prepare('train')" " 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": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"train_sampler[0] # Retrieving the first example" "train_sampler[0] # Retrieving the first example"
] ]
}, },
{ {
@@ -659,7 +700,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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": [], "outputs": [],
"source": [ "source": [
"handler_kwargs = {\n", "handler_kwargs = {\n",
" \"start_time\": \"2008-01-01\",\n", " \"start_time\": \"2008-01-01\",\n",
" \"end_time\": \"2020-08-01\",\n", " \"end_time\": \"2020-08-01\",\n",
" \"fit_start_time\": \"2008-01-01\",\n", " \"fit_start_time\": \"2008-01-01\",\n",
" \"fit_end_time\": \"2014-12-31\",\n", " \"fit_end_time\": \"2014-12-31\",\n",
" \"instruments\": MARKET,\n", " \"instruments\": MARKET,\n",
"}\n", "}\n",
"handler_conf = {\n", "handler_conf = {\n",
" \"class\": \"Alpha158\",\n", " \"class\": \"Alpha158\",\n",
@@ -735,6 +776,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"from qlib.contrib.data.handler import Alpha158\n", "from qlib.contrib.data.handler import Alpha158\n",
"\n",
"hd = Alpha158(**handler_kwargs)" "hd = Alpha158(**handler_kwargs)"
] ]
}, },
@@ -826,7 +868,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"hd.process_type # appending type" "hd.process_type # appending type"
] ]
}, },
{ {
@@ -857,16 +899,16 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"dataset_conf = {\n", "dataset_conf = {\n",
" \"class\": \"DatasetH\",\n", " \"class\": \"DatasetH\",\n",
" \"module_path\": \"qlib.data.dataset\",\n", " \"module_path\": \"qlib.data.dataset\",\n",
" \"kwargs\": {\n", " \"kwargs\": {\n",
" \"handler\": hd,\n", " \"handler\": hd,\n",
" \"segments\": {\n", " \"segments\": {\n",
" \"train\": (\"2008-01-01\", \"2014-12-31\"),\n", " \"train\": (\"2008-01-01\", \"2014-12-31\"),\n",
" \"valid\": (\"2015-01-01\", \"2016-12-31\"),\n", " \"valid\": (\"2015-01-01\", \"2016-12-31\"),\n",
" \"test\": (\"2017-01-01\", \"2020-08-01\"),\n", " \"test\": (\"2017-01-01\", \"2020-08-01\"),\n",
" },\n",
" },\n", " },\n",
" },\n",
"}" "}"
] ]
}, },
@@ -908,7 +950,8 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"model = init_instance_by_config({\n", "model = init_instance_by_config(\n",
" {\n",
" \"class\": \"LGBModel\",\n", " \"class\": \"LGBModel\",\n",
" \"module_path\": \"qlib.contrib.model.gbdt\",\n", " \"module_path\": \"qlib.contrib.model.gbdt\",\n",
" \"kwargs\": {\n", " \"kwargs\": {\n",
@@ -922,7 +965,8 @@
" \"num_leaves\": 210,\n", " \"num_leaves\": 210,\n",
" \"num_threads\": 20,\n", " \"num_threads\": 20,\n",
" },\n", " },\n",
"})" " }\n",
")"
] ]
}, },
{ {
@@ -938,7 +982,7 @@
" R.save_objects(trained_model=model)\n", " R.save_objects(trained_model=model)\n",
"\n", "\n",
" rec = R.get_recorder()\n", " rec = R.get_recorder()\n",
" rid = rec.id # save the record id\n", " rid = rec.id # save the record id\n",
"\n", "\n",
" # Inference and saving signal\n", " # Inference and saving signal\n",
" sr = SignalRecord(model, dataset, rec)\n", " sr = SignalRecord(model, dataset, rec)\n",
@@ -1001,12 +1045,11 @@
"\n", "\n",
"# backtest and analysis\n", "# backtest and analysis\n",
"with R.start(experiment_name=EXP_NAME, recorder_id=rid, resume=True):\n", "with R.start(experiment_name=EXP_NAME, recorder_id=rid, resume=True):\n",
"\n",
" # signal-based analysis\n", " # signal-based analysis\n",
" rec = R.get_recorder()\n", " rec = R.get_recorder()\n",
" sar = SigAnaRecord(rec)\n", " sar = SigAnaRecord(rec)\n",
" sar.generate()\n", " sar.generate()\n",
" \n", "\n",
" # portfolio-based analysis: backtest\n", " # portfolio-based analysis: backtest\n",
" par = PortAnaRecord(rec, port_analysis_config, \"day\")\n", " par = PortAnaRecord(rec, port_analysis_config, \"day\")\n",
" par.generate()" " par.generate()"
@@ -1137,7 +1180,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"label_df = dataset.prepare(\"test\", col_set=\"label\")\n", "label_df = dataset.prepare(\"test\", col_set=\"label\")\n",
"label_df.columns = ['label']" "label_df.columns = [\"label\"]"
] ]
}, },
{ {

16
examples/workflow_by_code.ipynb
View File

@@ -38,7 +38,7 @@
" # install qlib\n", " # install qlib\n",
" ! pip install --upgrade numpy\n", " ! pip install --upgrade numpy\n",
" ! pip install pyqlib\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", " # 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", " ! pip install pyyaml==5.4.1\n",
" # reload\n", " # reload\n",
@@ -50,7 +50,8 @@
" scripts_dir = Path(\"~/tmp/qlib_code/scripts\").expanduser().resolve()\n", " scripts_dir = Path(\"~/tmp/qlib_code/scripts\").expanduser().resolve()\n",
" scripts_dir.mkdir(parents=True, exist_ok=True)\n", " scripts_dir.mkdir(parents=True, exist_ok=True)\n",
" import requests\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", " with open(scripts_dir.joinpath(\"get_data.py\"), \"wb\") as fp:\n",
" fp.write(resp.content)" " fp.write(resp.content)"
] ]
@@ -61,14 +62,13 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"\n",
"import qlib\n", "import qlib\n",
"import pandas as pd\n", "import pandas as pd\n",
"from qlib.constant import REG_CN\n", "from qlib.constant import REG_CN\n",
"from qlib.utils import exists_qlib_data, init_instance_by_config\n", "from qlib.utils import exists_qlib_data, init_instance_by_config\n",
"from qlib.workflow import R\n", "from qlib.workflow import R\n",
"from qlib.workflow.record_temp import SignalRecord, PortAnaRecord\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", " print(f\"Qlib data is not found in {provider_uri}\")\n",
" sys.path.append(str(scripts_dir))\n", " sys.path.append(str(scripts_dir))\n",
" from get_data import GetData\n", " from get_data import GetData\n",
"\n",
" GetData().qlib_data(target_dir=provider_uri, region=REG_CN)\n", " GetData().qlib_data(target_dir=provider_uri, region=REG_CN)\n",
"qlib.init(provider_uri=provider_uri, region=REG_CN)" "qlib.init(provider_uri=provider_uri, region=REG_CN)"
] ]
@@ -169,7 +170,7 @@
" R.log_params(**flatten_dict(task))\n", " R.log_params(**flatten_dict(task))\n",
" model.fit(dataset)\n", " model.fit(dataset)\n",
" R.save_objects(trained_model=model)\n", " R.save_objects(trained_model=model)\n",
" rid = R.get_recorder().id\n" " rid = R.get_recorder().id"
] ]
}, },
{ {
@@ -238,7 +239,7 @@
"\n", "\n",
" # backtest & analysis\n", " # backtest & analysis\n",
" par = PortAnaRecord(recorder, port_analysis_config, \"day\")\n", " par = PortAnaRecord(recorder, port_analysis_config, \"day\")\n",
" par.generate()\n" " par.generate()"
] ]
}, },
{ {
@@ -256,6 +257,7 @@
"source": [ "source": [
"from qlib.contrib.report import analysis_model, analysis_position\n", "from qlib.contrib.report import analysis_model, analysis_position\n",
"from qlib.data import D\n", "from qlib.data import D\n",
"\n",
"recorder = R.get_recorder(recorder_id=ba_rid, experiment_name=\"backtest_analysis\")\n", "recorder = R.get_recorder(recorder_id=ba_rid, experiment_name=\"backtest_analysis\")\n",
"print(recorder)\n", "print(recorder)\n",
"pred_df = recorder.load_object(\"pred.pkl\")\n", "pred_df = recorder.load_object(\"pred.pkl\")\n",
@@ -317,7 +319,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"label_df = dataset.prepare(\"test\", col_set=\"label\")\n", "label_df = dataset.prepare(\"test\", col_set=\"label\")\n",
"label_df.columns = ['label']" "label_df.columns = [\"label\"]"
] ]
}, },
{ {

3
setup.py
View File

@@ -146,6 +146,9 @@ setup(
# References: https://github.com/python/typeshed/issues/8799 # References: https://github.com/python/typeshed/issues/8799
"mypy<0.981", "mypy<0.981",
"flake8", "flake8",
"nbqa",
"jupyter",
"nbconvert",
# The 5.0.0 version of importlib-metadata removed the deprecated endpoint, # 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. # 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 # To help ensure the dependencies of flake8 https://github.com/python/importlib_metadata/issues/406