<p>这个解决方案当然可以清理,但会产生您想要的输出。我已经在示例数据帧的构造中包含了您的初始条件:</p>
<pre><code>import pandas as pd
import numpy as np
df = pd.DataFrame({'Date': ['1990-01-01','1990-01-01','1990-01-01','1990-01-02','1990-01-02','1990-01-02','1990-01-03','1990-01-03','1990-01-03'],
'Symbol': ['A','B','C','A','B','C','A','B','C'],
'Close': [24, 72, 40, 21, 65, 45, 19, 70, 51],
'ATR': [2, 7, 3.4, 1.5, 6, 4.2, 2.5, 6.3, 5],
'Shares': [0, 0, 0, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
'Profit': [0, 0, 0, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]})
</code></pre>
<p>给出:</p>
^{pr2}$
<p>然后使用<code>groupby()</code>和<code>apply()</code>并全局跟踪您的{<cd3>}。我花了一点时间才意识到这个问题的本质要求您分别在两个独立的列上分组(<code>Symbol</code>和{<cd5>}):</p>
<pre><code>start = 10000
Equity = 10000
def calcs(x):
global Equity
if x.index[0]==0: return x #Skip first group
x['Shares'] = np.floor(Equity*0.02/x['ATR'])
x['Profit'] = x['Shares']*x['Close']
Equity += x['Profit'].sum()
return x
df = df.groupby('Date').apply(calcs)
df['Equity'] = df.groupby('Date')['Profit'].transform('sum')
df['Equity'] = df.groupby('Symbol')['Equity'].cumsum()+start
</code></pre>
<p>这就产生了:</p>
<pre><code> Date Symbol Close ATR Shares Profit Equity
0 1990-01-01 A 24 2.0 0.0 0.0 10000.0
1 1990-01-01 B 72 7.0 0.0 0.0 10000.0
2 1990-01-01 C 40 3.4 0.0 0.0 10000.0
3 1990-01-02 A 21 1.5 133.0 2793.0 17053.0
4 1990-01-02 B 65 6.0 33.0 2145.0 17053.0
5 1990-01-02 C 45 4.2 47.0 2115.0 17053.0
6 1990-01-03 A 19 2.5 136.0 2584.0 26885.0
7 1990-01-03 B 70 6.3 54.0 3780.0 26885.0
8 1990-01-03 C 51 5.0 68.0 3468.0 26885.0
</code></pre>