prices = pd.DataFrame({
    "a": [100, 110, 108, 120],
    "b": [800, 800, 750, 900],
    "c": [200, 250, 275, 300]
})
prices["a_daily_return"] = prices["a"].pct_change()

prices["a_shift"] = prices["a"].shift(1)
prices["a_daily_return2"] = prices["a"] / prices["a_shift"] - 1
# 对数收益率
prices["a_daily_log_return"] = np.log(prices["a"] / prices["a_shift"] )


returns = prices[["a","b","c"]].pct_change()

returns.head()
returns.plot()



prices.head()


a	b	c	a_shift	a_daily_return	a_daily_return2	a_daily_log_return
0	100	800	200	NaN	NaN	NaN	NaN
1	110	800	250	100.0	0.100000	0.100000	0.095310
2	108	750	275	110.0	-0.018182	-0.018182	-0.018349
3	120	900	300	108.0	0.111111	0.111111	0.105361

returns.head()

a	b	c
0	NaN	NaN	NaN
1	0.100000	0.0000	0.250000
2	-0.018182	-0.0625	0.100000
3	0.111111	0.2000	0.090909





df['rise20'] = (df['close'] - df['close'].shift(20)) * 100 / (df['close'].shift(20))

df['chg_abs'] = abs(df['close'] - df['close'].shift(1))
df['chg_abs_sum_20'] = df['chg_abs'].rolling(window=20).sum()

df['rsx20'] = (df['close'] - df['close'].shift(20)) * 100 / df['chg_abs_sum_20']