finlab.tools

finlab.tools.event_study

create_factor_data

create_factor_data(factor, adj_close, days=[5, 10, 20, 60], event=None)

create factor data, which contains future return

PARAMETER	DESCRIPTION
factor	factor data where index is datetime and columns is asset id TYPE: DataFrame
adj_close	adj close where index is datetime and columns is asset id TYPE: DataFrame
days	future return considered TYPE: List[int] DEFAULT: [5, 10, 20, 60]

Return

Analytic plots and tables

Warning

This function is not identical to finlab.ml.alphalens.create_factor_data

Examples:

現金增減資分析

from finlab.tools.event_study import create_factor_data
from finlab.tools.event_study import event_study

factor = data.get('price_earning_ratio:股價淨值比')
adj_close = data.get('etl:adj_close')
benchmark = data.get('benchmark_return:發行量加權股價報酬指數')

# create event dataframe
dividend_info = data.get('dividend_announcement')
v = dividend_info[['stock_id', '除權交易日']].set_index(['stock_id', '除權交易日'])
v['value'] = 1
event = v[~v.index.duplicated()].reset_index().drop_duplicates(
    subset=['stock_id', '除權交易日']
).pivot(index='除權交易日', columns='stock_id', values='value').notna()

# calculate factor_data
factor_data = create_factor_data({'pb':factor}, adj_close, event=event)

r = event_study(factor_data, benchmark, adj_close)

plt.bar(r.columns, r.mean().values)
plt.plot(r.columns, r.mean().cumsum().values)

event_study

event_study(factor_data, benchmark_adj_close, stock_adj_close, sample_period=(-45, -20), estimation_period=(-5, 20), plot=True)

Run event study and returns the abnormal returns of each stock on each day.

PARAMETER	DESCRIPTION
factor_data	factor data where index is datetime and columns is asset id TYPE: DataFrame
benchmark_adj_close	benchmark for CAPM TYPE: DataFrame
stock_adj_close	stock price for CAPM TYPE: DataFrame
sample_period	period for fitting CAPM TYPE: (int, int) DEFAULT: (-45, -20)
estimation_period	period for calculating alpha (abnormal return) TYPE: (int, int) DEFAULT: (-5, 20)
plot	plot the result TYPE: bool DEFAULT: True

Return

Abnormal returns of each stock on each day.

Examples:

現金增減資分析

from finlab.tools.event_study import create_factor_data
from finlab.tools.event_study import event_study

factor = data.get('price_earning_ratio:股價淨值比')
adj_close = data.get('etl:adj_close')
benchmark = data.get('benchmark_return:發行量加權股價報酬指數')

# create event dataframe
dividend_info = data.get('dividend_announcement')
v = dividend_info[['stock_id', '除權交易日']].set_index(['stock_id', '除權交易日'])
v['value'] = 1
event = v[~v.index.duplicated()].reset_index().drop_duplicates(
    subset=['stock_id', '除權交易日']
).pivot(index='除權交易日', columns='stock_id', values='value').notna()

# calculate factor_data
factor_data = create_factor_data({'pb':factor}, adj_close, event=event)

r = event_study(factor_data, benchmark, adj_close)

plt.bar(r.columns, r.mean().values)
plt.plot(r.columns, r.mean().cumsum().values)

plot_event_study

plot_event_study(returns)

Plot the event study for the given returns.

PARAMETER	DESCRIPTION
returns	A DataFrame containing the returns data. TYPE: DataFrame

Return

ax (matplotlib.axes.Axes): The axes object containing the plot.

finlab.tools.factor_analysis

calc_metric

calc_metric(factor, adj_close, days=[10, 20, 60, 120], func=corr)

計算因子

PARAMETER	DESCRIPTION
factor	因子 TYPE: DataFrame
adj_close	股價 TYPE: DataFrame
days	預測天數. Defaults to [10, 20, 60, 120]. TYPE: list DEFAULT: [10, 20, 60, 120]
func	計算函數. Defaults to corr. TYPE: function DEFAULT: corr

RETURNS	DESCRIPTION
	pd.DataFrame: 因子計算結果

Example

factor = data.indicator('RSI') adj_close = data.get('etl:adj_close') calc_metric(factor, adj_close)

date	factor_10	factor_20	factor_60	factor_120
2010-01-01	0.1	0.2	0.3	0.4
2010-01-02	0.1	0.2	0.3	0.4
2010-01-03	0.1	0.2	0.3	0.4
2010-01-04	0.1	0.2	0.3	0.4
2010-01-05	0.1	0.2	0.3	0.4

ic

ic(factor, adj_close, days=[10, 20, 60, 120])

計算因子的IC

PARAMETER	DESCRIPTION
factor	因子 TYPE: DataFrame
adj_close	股價 TYPE: DataFrame
days	預測天數. Defaults to [10, 20, 60, 120]. TYPE: list DEFAULT: [10, 20, 60, 120]

RETURNS	DESCRIPTION
	pd.DataFrame: 因子計算結果

Example

factor = data.indicator('RSI') adj_close = data.get('etl:adj_close') calc_metric(factor, adj_close)

date	factor_10	factor_20	factor_60	factor_120
2010-01-01	0.1	0.2	0.3	0.4
2010-01-02	0.1	0.2	0.3	0.4
2010-01-03	0.1	0.2	0.3	0.4
2010-01-04	0.1	0.2	0.3	0.4
2010-01-05	0.1	0.2	0.3	0.4