我正在嘗試使用 RSI 進行技術分析，其中涉及 2 個收益和損失串列，然后獲取 2 個串列的平均值。出于某種原因，當使用 sum() 時，我收到 2 個串列的相同值。手動添加串列的總和時，我看到不同的結果。這就是我所擁有的：
def calculate_avg_gain(in_list : list) -> tuple:
gain_list = []
loss_list = []
for i, close in enumerate(in_list):
try:
change = close - in_list[i-1]
except IndexError:
change = 0.0
if change >= 0.0:
gain_list.append(change)
loss_list.append(0.0)
else:
gain_list.append(0.0)
loss_list.append(abs(change))
avg_gain = sum(gain_list) / 14
avg_loss = sum(loss_list) / 14
return avg_gain, avg_loss
當每個條目有 14 個條目時，我介入了代碼：
gain_list = [1.1800000000000068, 0.0, 0.0, 0.10000000000002274, 0.0, 0.44999999999998863, 0.030000000000001137, 0.0, 0.160000000000025, 0.0, 0.0, 0.15000000000000568, 0.0, 0.05000000000001137]
loss_list = [0.0, 0.19999999999998863, 0.10999999999998522, 0.0, 0.0, 0.0, 0.5, 0.3299999999999841, 0.0, 0.47999999999998977, 0.0, 0.10000000000002274, 0.25, 0.0]
avg_gain = 0.15142857142857583
avg_loss = 0.1407142857142836
兩者的總和為 2.12，這是我得到的 loss_list 總和，而我得到的 Gain_list 總和為 2.1799。我錯過了什么嗎？
uj5u.com熱心網友回復：
如果您看到這里的問題是，無論何時執行in_list[-1]它都不會 throw IndexError，但它會使用最后一個元素in_list[len(list)-1]
In [4]: def calculate_avg_gain(in_list : list) -> tuple:
...: gain_list = []
...: loss_list = []
...: for i, close in enumerate(in_list):
...: try:
...: change = close - in_list[i-1]
...: except IndexError:
...: change = 0.0
...: if change >= 0.0:
...: gain_list.append(change)
...: loss_list.append(0.0)
...: else:
...: gain_list.append(0.0)
...: loss_list.append(abs(change))
...: print(gain_list, loss_list, change)
...: avg_gain = sum(gain_list) / 14
...: avg_loss = sum(loss_list) / 14
...: return avg_gain, avg_loss
...:
In [5]: calculate_avg_gain(a)
[0.0] [4] -4
[0.0, 1] [4, 0.0] 1
[0.0, 1, 1] [4, 0.0, 0.0] 1
[0.0, 1, 1, 1] [4, 0.0, 0.0, 0.0] 1
[0.0, 1, 1, 1, 1] [4, 0.0, 0.0, 0.0, 0.0] 1
Out[5]: (0.2857142857142857, 0.2857142857142857)
您可以將代碼更新為以下內容：
In [6]: def calculate_avg_gain(in_list : list) -> tuple:
...: gain_list = []
...: loss_list = []
...: for i, close in enumerate(in_list):
...: if i == 0:
...: continue
...: change = close - in_list[i-1]
...: if change >= 0.0:
...: gain_list.append(change)
...: loss_list.append(0.0)
...: else:
...: gain_list.append(0.0)
...: loss_list.append(abs(change))
...: print(gain_list, loss_list, change)
...: avg_gain = sum(gain_list) / 14
...: avg_loss = sum(loss_list) / 14
...: return avg_gain, avg_loss
...:
In [7]: calculate_avg_gain(a)
[1] [0.0] 1
[1, 1] [0.0, 0.0] 1
[1, 1, 1] [0.0, 0.0, 0.0] 1
[1, 1, 1, 1] [0.0, 0.0, 0.0, 0.0] 1
Out[7]: (0.2857142857142857, 0.0)
如果你想要一個單線的變化，你可以執行以下操作
>>> [j-i for i, j in zip(t[:-1], t[1:])]
[2, 3]
然后，如果變化> 0，則可以增加收益，如果不增加損失。
其他方法：
for i in range(1, len(in_list)):
change = in_list[i] - in_list[i-1]
如果你已經NumPy在你的專案中使用，你可以做
import numpy as np
in_list = np.array([5, 4, 89, 12, 32, 45])
# Calculating difference list
diff_list = np.diff(in_list)
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