我正在尝试在 Pandas 中进行交易回溯测试，但使用 np.where() 有条件更新其他列的“if”语句存在一些问题。
我的初始 df，其中信号指示是否买入/卖出或两者都不买入 (1/-1/0)，根据这些信号我想更新现金、持有、值(value)和总计列。
open high low close change signal Cash Hold Value Total
time
2017-09-09 03:01:00 4255.000000 4256.799805 4233.600098 4252.799805 -0.000065 0 10000.0 0.0 0.0 10000.0
2017-09-09 03:02:00 4251.399902 4258.500000 4247.500000 4258.399902 0.002046 1 10000.0 0.0 0.0 10000.0
2017-09-09 03:03:00 4256.500000 4289.299805 4256.500000 4273.700195 0.001262 1 10000.0 0.0 0.0 10000.0
2017-09-09 03:04:00 4273.100098 4299.899902 4262.580566 4284.100098 0.001905 1 10000.0 0.0 0.0 10000.0
2017-09-09 03:05:00 4291.200195 4299.799805 4284.200195 4289.899902 -0.000854 -1 10000.0 0.0 0.0 10000.0
2017-09-09 03:06:00 4295.000000 4298.799805 4279.500000 4279.500000 -0.000047 0 10000.0 0.0 0.0 10000.0
2017-09-09 03:07:00 4278.000000 4278.299805 4277.000000 4277.799805 -0.000244 0 10000.0 0.0 0.0 10000.0
我可以通过根据信号手动调用以下每个函数来做到这一点:
def buy_update(i=i):
pf['Cash'].iloc[i] = pf['Cash'].iloc[i-1] - trade_size
pf['Holdings'].iloc[i] = pf['Holdings'].iloc[i-1] + (trade_size / pf['close'].iloc[i])
pf['Holdings Value'].iloc[i] = pf['close'].iloc[i] * pf['Holdings'].iloc[i] # Update Values
pf['Total Holding'].iloc[i] = pf['Cash'].iloc[i] + pf['Holdings Value'].iloc[i] # Update Values
def sell_update(i=i):
pf['Cash'].iloc[i] = (pf['Cash'].iloc[i-1] + (pf['Holdings'].iloc[i-1] * pf['close'].iloc[i])) # get cash for sale
pf['Holdings'].iloc[i] = 0 # Sell down all assets
pf['Holdings Value'].iloc[i] = pf['close'].iloc[i] * pf['Holdings'].iloc[i] # Update Values
pf['Total Holding'].iloc[i] = pf['Cash'].iloc[i] + pf['Holdings Value'].iloc[i] # Update Value
def no_action(i=i):
pf['Cash'].iloc[i] = pf['Cash'].iloc[i-1]
pf['Holdings'].iloc[i] = pf['Holdings'].iloc[i-1]
pf['Holdings Value'].iloc[i] = pf['close'].iloc[i] * pf['Holdings'].iloc[i] # Update Values
pf['Total Holding'].iloc[i] = pf['Cash'].iloc[i] + pf['Holdings Value'].iloc[i] # Update Values
然后生成:
open high low close change signal Cash Hold Value Total
time
2017-09-09 03:01:00 4255.000000 4256.799805 4233.600098 4252.799805 -0.000065 0 10000.00000 0.000000 0.000000 10000.000000
2017-09-09 03:02:00 4251.399902 4258.500000 4247.500000 4258.399902 0.002046 1 9900.00000 0.023483 100.000000 10000.000000
2017-09-09 03:03:00 4256.500000 4289.299805 4256.500000 4273.700195 0.001262 1 9800.00000 0.046882 200.359297 10000.359297
2017-09-09 03:04:00 4273.100098 4299.899902 4262.580566 4284.100098 0.001905 1 9700.00000 0.070224 300.846864 10000.846864
2017-09-09 03:05:00 4291.200195 4299.799805 4284.200195 4289.899902 -0.000854 -1 10001.25415 0.000000 0.000000 10001.254150
2017-09-09 03:06:00 4295.000000 4298.799805 4279.500000 4279.500000 -0.000047 0 10001.25415 0.000000 0.000000 10001.254150
2017-09-09 03:07:00 4278.000000 4278.299805 4277.000000 4277.799805 -0.000244 0 10001.25415 0.000000 0.000000 10001.254150
我认为嵌套的 np.where() 可以根据信号列调用正确的函数，但我没有任何运气。下面循环遍历每一行。
for i in range(len(pf)):
np.where(pf['signal'].iloc[i] == -1, sell_update(i), np.where(pf['signal'].iloc[i] == 1, buy_update(i), no_action(i)))
print(i)
我认为它目前会调用每个函数 - 卖出，然后买入，然后不调用(每个函数都会覆盖最后一个)，并产生 SettingWithCopyWarning 警告。
此外，每行的 for 循环显然非常慢，有没有办法对其进行矢量化？
最佳答案
当计算代码变得复杂时，很难对其进行矢量化。由于 pandas 中逐个元素的处理速度很慢，您可以将数据帧转换为字典列表，然后进行计算，这里是一个使用 cytoolz 的示例:
import io
import pandas as pd
text="""time open high low close change signal Cash Hold Value Total
2017-09-09 03:01:00 4255.000000 4256.799805 4233.600098 4252.799805 -0.000065 0 10000.0 0.0 0.0 10000.0
2017-09-09 03:02:00 4251.399902 4258.500000 4247.500000 4258.399902 0.002046 1 10000.0 0.0 0.0 10000.0
2017-09-09 03:03:00 4256.500000 4289.299805 4256.500000 4273.700195 0.001262 1 10000.0 0.0 0.0 10000.0
2017-09-09 03:04:00 4273.100098 4299.899902 4262.580566 4284.100098 0.001905 1 10000.0 0.0 0.0 10000.0
2017-09-09 03:05:00 4291.200195 4299.799805 4284.200195 4289.899902 -0.000854 -1 10000.0 0.0 0.0 10000.0
2017-09-09 03:06:00 4295.000000 4298.799805 4279.500000 4279.500000 -0.000047 0 10000.0 0.0 0.0 10000.0
2017-09-09 03:07:00 4278.000000 4278.299805 4277.000000 4277.799805 -0.000244 0 10000.0 0.0 0.0 10000.0"""
df = pd.read_csv(io.StringIO(text), delim_whitespace=True)
trade_size = 100
import cytoolz
def f(p, c):
change = c["signal"]
if change == 0:
cash = c["Cash"]
hold = c["Hold"]
elif change == 1:
cash = p["Cash"] - trade_size
hold = p["Hold"] + trade_size / c["close"]
elif change == -1:
cash = p["Cash"] + p["Hold"] * c["close"]
hold = 0
return cytoolz.merge(c, {"Cash":cash, "Hold":hold})
pd.DataFrame(list(cytoolz.accumulate(f, df.to_dict("records"))))
关于python - Pandas - 使用 np.where() 有条件地更新列，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/48896723/