RDAgent

{
    "Turnover_Rate_Factor": {
        "description": "基于 20 日平均换手率的传统因子，根据市值进行调整，并通过应用信息分布理论进一步改进。",
        "formulation": "\\text{Adjusted Turnover Rate} = \\frac{\\text{mean}(20\\text{-day turnover rate})}{\\text{Market Capitalization}}",
        "variables": {
            "20-day turnover rate": "过去 20 天的平均换手率。",
            "Market Capitalization": "公司已发行股票的总市值。"
        },
        "Category": "Fundamentals",
        "Difficulty": "Easy",
        "gt_code": "import pandas as pd\n\ndata_f = pd.read_hdf('daily_f.h5')\n\ndata = data_f.reset_index()\nwindow_size = 20\n\nnominator=data.groupby('instrument')[['TurnoverRate_30D']].rolling(window=window_size).mean().reset_index(0, drop=True)\n# transfer to series\nnew=nominator['TurnoverRate_30D']\ndata['Turnover_Rate_Factor']=new/data['TradableACapital']\n\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(data['Turnover_Rate_Factor']).set_index(data_f.index)\n\n# transfer the result to series\nresult=result['Turnover_Rate_Factor']\nresult.to_hdf(\"result.h5\", key=\"data\")"
    },
    "PctTurn20": {
        "description": "一个代表过去 20 个交易日换手率百分比变化的因子，已进行市值中性化。",
        "formulation": "\\text{PctTurn20} = \\frac{1}{N} \\sum_{i=1}^{N} \\left( \\frac{\\text{Turnover}_{i, t} - \\text{Turnover}_{i, t-20}}{\\text{Turnover}_{i, t-20}} \\right)",
        "variables": {
            "N": "市场中股票数量。",
            "Turnover_{i, t}": "股票 i 在 t 日的换手率。",
            "Turnover_{i, t-20}": "股票 i 在 t-20 日的换手率。"
        },
        "Category": "Volume&Price",
        "Difficulty": "Medium",
        "gt_code": "import pandas as pd\nfrom statsmodels import api as sm\n\ndef fill_mean(s: pd.Series) -> pd.Series:\n    return s.fillna(s.mean()).fillna(0.0)\n\ndef market_value_neutralize(s: pd.Series, mv: pd.Series) -> pd.Series:\n    s = s.groupby(\"datetime\", group_keys=False).apply(fill_mean)\n    mv = mv.groupby(\"datetime\", group_keys=False).apply(fill_mean)\n\n    df_f = mv.to_frame(\"MarketValue\")\n    df_f[\"const\"] = 1\n    X = df_f[[\"MarketValue\", \"const\"]]\n\n    # Perform the Ordinary Least Squares (OLS) regression\n    model = sm.OLS(s, X)\n    results = model.fit()\n\n    # Calculate the residuals\n    df_f[\"residual\"] = results.resid\n    df_f[\"norm_resi\"] = df_f.groupby(level=\"datetime\", group_keys=False)[\"residual\"].apply(\n        lambda x: (x - x.mean()) / x.std(),\n    )\n    return df_f[\"norm_resi\"]\n\n\n# get_turnover\ndf_pv = pd.read_hdf(\"daily_pv.h5\", key=\"data\")\ndf_f = pd.read_hdf(\"daily_f.h5\", key=\"data\")\nturnover = df_pv[\"$money\"] / df_f[\"TradableMarketValue\"]\n\nf = turnover.groupby(\"instrument\").pct_change(periods=20)\n\nf_neutralized = market_value_neutralize(f, df_f[\"TradableMarketValue\"])\n\nf_neutralized.to_hdf(\"result.h5\", key=\"data\")"
    },
    "PB_ROE": {
        "description": "使用 PB 和 ROE 之间的排名差异构建，用 PB 和 ROE 替换原始 PB 和 ROE 以获得重构的因子值。",
        "formulation": "\\text{rank}(PB\\_t) - rank(ROE_t)",
        "variables": {
            "\\text{rank}(PB_t)": "在 t 时刻对横截面上的 PB 进行排名。",
            "\\text{rank}(ROE_t)": "在 t 时刻对横截面上的单季度 ROE 进行排名。"
        },
        "Category": "High-Frequency",
        "Difficulty": "Hard",
        "gt_code": "#!/usr/bin/env python\n\nimport pandas as pd\n\ndata_f = pd.read_hdf('daily_f.h5')\n\ndata = data_f.reset_index()\n\n# Calculate the rank of PB and ROE\ndata['PB_rank'] = data.groupby('datetime')['B/P'].rank()\ndata['ROE_rank'] = data.groupby('datetime')['ROE'].rank()\n\n# Calculate the difference between the ranks\ndata['PB_ROE'] = data['PB_rank'] - data['ROE_rank']\n\n# set the datetime and instrument as index and drop the original index\nresult=pd.DataFrame(data['PB_ROE']).set_index(data_f.index)\n\n# transfer the result to series\nresult=result['PB_ROE']\nresult.to_hdf(\"result.h5\", key=\"data\")"
    }
}