finlab.tools

from finlab import data
from finlab.tools.event_study import create_factor_data, 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')

# 事件：每檔股票的除權交易日
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()

factor_data = create_factor_data({'pb': factor}, adj_close, event=event)
abnormal_returns = event_study(factor_data, benchmark, adj_close)

from finlab.tools.event_study import create_factor_data

factor_data = create_factor_data({'pb': factor}, adj_close, event=event)
factor_data.head()

from finlab.tools.event_study import plot_event_study

plot_event_study(abnormal_returns)

from finlab import data
from finlab.tools import factor_analysis as fa

marketcap = data.get('etl:market_value')
revenue = data.get('monthly_revenue:當月營收')
close = data.get('price:收盤價')
revenue_growth = revenue.average(3) / revenue.average(12)
momentum = close / close.shift(20)

# 布林因子 → 特徵（日期 × 股票）與下個月的超額報酬標籤
factors = {
    'small_cap': marketcap.rank(pct=True, axis=1) < 0.3,  # 小市值
    'revenue_growth': revenue_growth.rank(pct=True, axis=1) > 0.7,  # 營收成長
    'momentum': momentum.rank(pct=True, axis=1) > 0.7,  # 動能
}
features, labels = fa.generate_features_and_labels(factors, resample='ME')

factor_return = fa.calc_factor_return(features, labels)
factor_return.cumsum().plot(title='因子累積報酬')
print(factor_return.mean())  # 每月選出股票的平均超額報酬

from finlab.ml import feature

features_raw = feature.combine(
    {'small_cap': -marketcap, 'revenue_growth': revenue_growth, 'momentum': momentum},
    resample='ME',
)
ic = fa.calc_ic(features_raw, labels, rank=True)
print(ic.mean() / ic.std())  # IC_IR

shapley = fa.calc_shapley_values(features, labels)
print(shapley.mean())

# 12 個月窗口的集中度（擁擠度）
centrality = fa.calc_centrality(factor_return, 12)

print(fa.calc_regression_stats(centrality))

from finlab import data
from finlab.backtest import sim
from finlab.tools import factor_analysis as fa

# 步驟 1：建立因子
marketcap = data.get('etl:market_value')
revenue = data.get('monthly_revenue:當月營收')
close = data.get('price:收盤價')
revenue_growth = revenue.average(3) / revenue.average(12)
momentum = close / close.shift(20)

factors = {
    'small_cap': marketcap.rank(pct=True, axis=1) < 0.3,  # 小市值
    'revenue_growth': revenue_growth.rank(pct=True, axis=1) > 0.7,  # 營收成長
    'momentum': momentum.rank(pct=True, axis=1) > 0.7,  # 動能
}

# 步驟 2：特徵與標籤（下個月的超額報酬）
features, labels = fa.generate_features_and_labels(factors, resample='ME')

# 步驟 3：分析各因子表現
print("=== 平均每月因子報酬 ===")
print(fa.calc_factor_return(features, labels).mean())

# 步驟 4：計算 Shapley values（貢獻度）
print("=== 平均 Shapley 值 ===")
print(fa.calc_shapley_values(features, labels).mean())

# 步驟 5：回測策略
position = factors['small_cap'] & factors['revenue_growth'] & factors['momentum']
report = sim(position, resample='M')
report.display()

stats = report.get_stats()
print(f"年化報酬: {stats['cagr']:.2%}")
print(f"夏普率: {stats['daily_sharpe']:.2f}")
print(f"最大回撤: {stats['max_drawdown']:.2%}")