45 lines
1.9 KiB
Python
45 lines
1.9 KiB
Python
import pandas as pd
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class Selector:
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def select(self, codes: [str], df: pd.DataFrame) -> [str]:
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return codes
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class Strategy:
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def __init__(self, selectors: [Selector]):
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self.selectors = selectors
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def select(self, codes: [str], df: pd.DataFrame) -> [str]:
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return list(map(lambda code: self.selectors.select(code, df), codes))
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class PeriodSelector(Selector):
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def __init__(self, period: int = 5):
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self.__period = period
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def select(self, codes: [str], df: pd.DataFrame) -> [str]:
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size_df = df.groupby("code").size()
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return list(filter(lambda code: size_df[code] > self.__period, codes))
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class PyramidSelector(Selector):
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def select(self, codes: [str], df: pd.DataFrame) -> [str]:
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target_df = df[df["code"].isin(codes)]
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target_df["score"] = 0
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group_df = target_df.groupby("code")
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target_df["prev_total_stockholder_interest"] = group_df["total_stockholder_interest"].shift(1)
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target_df["roe"] = target_df["net_income"] / ((target_df["prev_total_stockholder_interest"] + target_df["total_stockholder_interest"]) / 2)
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target_df["average_roe"] = target_df["roe"].mean()
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target_df[target_df["average_roe"] >= 35] = target_df["score"] + 550
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target_df[(target_df["average_roe"] < 35) & (target_df["average_roe"] >= 30)] = target_df["score"] + 500
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target_df[(target_df["average_roe"] < 30) & (target_df["average_roe"] >= 25)] = target_df["score"] + 450
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target_df[(target_df["average_roe"] < 25) & (target_df["average_roe"] >= 15)] = target_df["score"] + 300
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target_df[(target_df["average_roe"] < 15) & (target_df["average_roe"] >= 10)] = target_df["score"] + 250
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target_df["prev_total_assets"] = group_df["total_assets"].shift(1)
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target_df["roa"] = target_df["net_income"] / ((target_df["prev_total_assets"] + target_df["total_assets"]) / 2)
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return super().select(codes, df)
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