Create reports_html.py

examples/reports_html.py

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+# %%
+import os
+import sys
+from pathlib import Path
+
+# 修改当前目录到上层目录，方便跨不同IDE中使用
+pwd = str(Path(__file__).parents[1])
+os.chdir(pwd)
+sys.path.append(pwd)
+# ===============
+import multiprocessing
+import time
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import polars as pl
+from loguru import logger
+
+from alphainspect.reports import create_1x3_sheet, fig_to_img, html_template
+from alphainspect.utils import with_factor_quantile
+
+INPUT1_PATH = r'data/data.parquet'
+OUTPUT_PATH = r'output'
+output = Path(OUTPUT_PATH)
+output.mkdir(parents=True, exist_ok=True)
+
+
+def func(kv):
+    name, factors = kv
+    axvlines = ('2020-01-01', '2024-01-01',)
+    fwd_ret_1 = 'RETURN_OO_05'  # 计算净值用的1日收益率
+    quantiles = 5
+
+    tbl = {}
+    df_mean = {}
+    df_std = {}
+    df_last = {}
+    imgs = []
+
+    df = pl.read_parquet(INPUT1_PATH)
+    for factor in factors:
+        df = with_factor_quantile(df, factor, quantiles=quantiles, factor_quantile=f'_fq_{factor}')
+
+    for factor in factors:
+        fig, ic_dict, hist_dict, cum, avg, std = create_1x3_sheet(df, factor, fwd_ret_1, factor_quantile=f'_fq_{factor}', axvlines=axvlines)
+
+        s1 = cum.iloc[-1]
+        df_last[factor] = s1
+        df_mean[factor] = avg
+        df_std[factor] = std
+
+        s2 = {'monotonic': np.sign(s1.diff()).sum()}
+        s3 = pd.Series(s2 | ic_dict | hist_dict)
+        tbl[factor] = pd.concat([s1, s3])
+        imgs.append(fig_to_img(fig))
+
+    df_last = pd.DataFrame(df_last)
+    df_mean = pd.DataFrame(df_mean)
+    df_std = pd.DataFrame(df_std)
+
+    # 各指标柱状图
+    tbl = pd.DataFrame(tbl)
+    fig, axes = plt.subplots(3, 1, figsize=(12, 6), sharex=True)
+    ax = df_last.plot.bar(ax=axes[0])
+    ax.set_title(f'Last Total Return By Quantile')
+    ax = df_mean.plot.bar(ax=axes[1])
+    ax.set_title(f'Mean Return By Quantile')
+    ax = df_std.plot.bar(ax=axes[2])
+    ax.set_title(f'Std Return By Quantile')
+    plt.xticks(rotation=0)
+    fig.tight_layout()
+    imgs.insert(0, fig_to_img(fig))
+
+    # 表格
+    txt1 = tbl.T.to_html(float_format=lambda x: format(x, '.4f'))
+    # 图
+    txt2 = '\n'.join(imgs)
+    tpl = html_template.replace('{{body}}', f'{txt1}\n{txt2}')
+
+    with open(str(output / f'{name}.html'), "w", encoding="utf-8") as f:
+        f.write(tpl)
+
+    return 0
+
+
+if __name__ == '__main__':
+    factors_kv = {
+        "SMA": ['SMA_005', 'SMA_010', 'SMA_020', ],
+        "STD": ['STD_005', 'STD_010', 'STD_020', ],
+    }
+    t0 = time.perf_counter()
+    logger.info('开始')
+    # 没必要设置太大，因为部分计算使用的polars多线程，会将CPU跑满
+    with multiprocessing.Pool(2) as pool:
+        print(list(pool.map(func, factors_kv.items())))
+    logger.info('结束')
+    logger.info(f'耗时：{time.perf_counter() - t0:.2f}s')
+    os.system(f'explorer.exe "{output}"')