带有 pandas 数据帧的 for 循环中的 if 语句
[英]if statement in for loop with pandas dataframes
问题描述
I am making a Dollar Cost Average code where I want to choose between 2 equations.
我正在制作一个美元成本平均代码，我想在两个方程式之间进行选择。 I made an excel spreadsheet that I'm trying to portover to python.我制作了一个 excel 电子表格，我正在尝试将其移植到 python。 I've gotten pretty far except for the last step.除了最后一步，我已经走了很远。 The last step has had me searching for a solution for 3 weeks now.最后一步已经让我寻找解决方案 3 周了。 The errors happen when I try a for loop in a df when looping through.当我在循环时尝试在 df 中进行 for 循环时会发生错误。 I would like to check a column with an if the statement.我想检查带有 if 语句的列。 If is true then do an equation if false do another equation.如果为真，则做一个方程式，如果为假，则再做一个方程式。 I can get the for loop to work and I can the if statements to work, but not combined.我可以让 for 循环工作，我可以让 if 语句工作，但不能合并。 See all commented out code for whats been tried.查看所有已尝试过的注释代码。 I have tried np.where instead of the if statements as well.我也尝试过 np.where 而不是 if 语句。 I have tried.loc.我试过.loc。 I have tried lamda.我试过拉姆达。 I have tried list comp.我试过列表比较。 Nothing is working please help.没有任何工作，请帮助。 FYI the code referring is ['trend bal'] column.仅供参考，引用的代码是 ['trend bal'] 列。 ***see end with correct code. ***以正确的代码结尾。
What the df looks like: df 的样子：
Index timestamp Open High Low ... rate account bal invested ST_10_1.0 if trend
0 0 8/16/2021 4382.439941 4444.350098 4367.729980 ... 1.000000 $10,000.00 10000 1 0
1 1 8/23/2021 4450.290039 4513.330078 4450.290039 ... 0.015242 $10,252.42 10100 1 0
2 2 8/30/2021 4513.759766 4545.850098 4513.759766 ... 0.005779 $10,411.67 10200 1 0
3 3 9/6/2021 4535.379883 4535.379883 4457.660156 ... -0.016944 $10,335.25 10300 1 0
4 4 9/13/2021 4474.810059 4492.990234 4427.759766 ... -0.005739 $10,375.93 10400 1 0
5 5 9/20/2021 4402.950195 4465.399902 4305.910156 ... 0.005073 $10,528.57 10500 1 0
6 6 9/27/2021 4442.120117 4457.299805 4288.520020 ... -0.022094 $10,395.95 10600 1 0
7 7 10/4/2021 4348.839844 4429.970215 4278.939941 ... 0.007872 $10,577.79 10700 1 0
8 8 10/11/2021 4385.439941 4475.819824 4329.919922 ... 0.018225 $10,870.57 10800 1 0
9 9 10/18/2021 4463.720215 4559.669922 4447.470215 ... 0.016445 $11,149.33 10900 1 0
10 10 10/25/2021 4553.689941 4608.080078 4537.359863 ... 0.013307 $11,397.70 11000 1 0
11 11 11/1/2021 4610.620117 4718.500000 4595.060059 ... 0.020009 $11,725.75 11100 1 0
12 12 11/8/2021 4701.479980 4714.919922 4630.859863 ... -0.003125 $11,789.11 11200 1 0
13 13 11/15/2021 4689.299805 4717.750000 4672.779785 ... 0.003227 $11,927.15 11300 1 0
14 14 11/22/2021 4712.000000 4743.830078 4585.430176 ... -0.021997 $11,764.79 11400 1 0
15 15 11/29/2021 4628.750000 4672.950195 4495.120117 ... -0.012230 $11,720.92 11500 -1 100
16 16 12/6/2021 4548.370117 4713.569824 4540.509766 ... 0.038249 $12,269.23 11600 -1 100
17 17 12/13/2021 4710.299805 4731.990234 4600.220215 ... -0.019393 $12,131.29 11700 1 0
18 18 12/20/2021 4587.899902 4740.740234 4531.100098 ... 0.022757 $12,507.36 11800 1 0
19 19 12/27/2021 4733.990234 4808.930176 4733.990234 ... 0.008547 $12,714.25 11900 1 0
20 20 1/3/2022 4778.140137 4818.620117 4662.740234 ... -0.018705 $12,576.44 12000 1 0
21 21 1/10/2022 4655.339844 4748.830078 4582.240234 ... -0.003032 $12,638.31 12100 1 0
22 22 1/17/2022 4632.240234 4632.240234 4395.339844 ... -0.056813 $12,020.29 12200 1 0
23 23 1/24/2022 4356.319824 4453.229980 4222.620117 ... 0.007710 $12,212.97 12300 -1 100
24 24 1/31/2022 4431.790039 4595.310059 4414.020020 ... 0.015497 $12,502.23 12400 -1 100
25 25 2/7/2022 4505.750000 4590.029785 4401.410156 ... -0.018196 $12,374.75 12500 1 0
26 26 2/14/2022 4412.609863 4489.549805 4327.220215 ... -0.015790 $12,279.35 12600 1 0
27 27 2/21/2022 4332.740234 4385.339844 4114.649902 ... 0.008227 $12,480.38 12700 1 0
28 28 2/28/2022 4354.169922 4416.779785 4279.540039 ... -0.012722 $12,421.61 12800 1 0
29 29 3/7/2022 4327.009766 4327.009766 4157.870117 ... -0.028774 $12,164.19 12900 -1 100
30 30 3/14/2022 4202.750000 4465.399902 4161.720215 ... 0.061558 $13,012.99 13000 -1 100
31 31 3/21/2022 4462.399902 4546.029785 4424.299805 ... 0.017911 $13,346.07 13100 1 0
32 32 3/28/2022 4541.089844 4637.299805 4507.569824 ... 0.000616 $13,454.30 13200 1 0
33 33 4/4/2022 4547.970215 4593.450195 4450.040039 ... -0.012666 $13,383.88 13300 1 0
34 34 4/11/2022 4462.640137 4471.000000 4381.339844 ... -0.021320 $13,198.53 13400 1 0
35 35 4/18/2022 4385.629883 4512.939941 4267.620117 ... -0.027503 $12,935.53 13500 -1 100
36 36 4/25/2022 4255.339844 4308.450195 4124.279785 ... -0.032738 $12,612.05 13600 -1 100
37 37 5/2/2022 4130.609863 4307.660156 4062.510010 ... -0.002079 $12,685.83 13700 -1 100
38 38 5/9/2022 4081.270020 4081.270020 3858.870117 ... -0.024119 $12,479.86 13800 -1 100
39 39 5/16/2022 4013.020020 4090.719971 3810.320068 ... -0.030451 $12,199.84 13900 -1 100
40 40 5/23/2022 3919.419922 4158.490234 3875.129883 ... 0.065844 $13,103.12 14000 -1 100
41 41 5/30/2022 4151.089844 4177.509766 4073.850098 ... -0.011952 $13,046.51 14100 1 0
42 42 6/6/2022 4134.720215 4168.779785 3900.159912 ... -0.050548 $12,487.03 14200 1 0
43 43 6/13/2022 3838.149902 3838.149902 3636.870117 ... -0.057941 $11,863.52 14300 -1 100
44 44 6/20/2022 3715.310059 3913.649902 3715.310059 ... 0.064465 $12,728.31 14400 -1 100
45 45 6/27/2022 3920.760010 3945.860107 3738.669922 ... -0.022090 $12,547.14 14500 -1 100
46 46 7/4/2022 3792.610107 3918.500000 3742.060059 ... 0.019358 $12,890.03 14600 -1 100
47 47 7/11/2022 3880.939941 3880.939941 3721.560059 ... -0.009289 $12,870.29 14700 -1 100
48 48 7/18/2022 3883.790039 4012.439941 3818.629883 ... 0.025489 $13,298.35 14800 -1 100
49 49 7/25/2022 3965.719971 4140.149902 3910.739990 ... 0.042573 $13,964.51 14900 1 0
50 50 8/1/2022 4112.379883 4167.660156 4079.810059 ... 0.003607 $14,114.88 15000 1 0
51 51 8/8/2022 4155.930176 4280.470215 4112.089844 ... 0.032558 $14,674.44 15100 1 0
52 52 8/15/2022 4269.370117 4325.279785 4253.080078 ... 0.000839 $14,786.75 15200 1 0
53 53 8/19/2022 4266.310059 4266.310059 4218.700195 ... -0.012900 $14,696.00 15300 1 0
What it should look like:它应该是什么样子：
Index timestamp Open High Low ... account bal invested ST_10_1.0 if trend trend bal
0 0 8/16/2021 4382.439941 4444.350098 4367.729980 ... $10,000.00 10000 1 0 $10,000.00
1 1 8/23/2021 4450.290039 4513.330078 4450.290039 ... $10,252.42 10100 1 0 $10,252.42
2 2 8/30/2021 4513.759766 4545.850098 4513.759766 ... $10,411.67 10200 1 0 $10,411.67
3 3 9/6/2021 4535.379883 4535.379883 4457.660156 ... $10,335.25 10300 1 0 $10,335.25
4 4 9/13/2021 4474.810059 4492.990234 4427.759766 ... $10,375.93 10400 1 0 $10,375.93
5 5 9/20/2021 4402.950195 4465.399902 4305.910156 ... $10,528.57 10500 1 0 $10,528.57
6 6 9/27/2021 4442.120117 4457.299805 4288.520020 ... $10,395.95 10600 1 0 $10,395.95
7 7 10/4/2021 4348.839844 4429.970215 4278.939941 ... $10,577.79 10700 1 0 $10,577.79
8 8 10/11/2021 4385.439941 4475.819824 4329.919922 ... $10,870.57 10800 1 0 $10,870.57
9 9 10/18/2021 4463.720215 4559.669922 4447.470215 ... $11,149.33 10900 1 0 $11,149.33
10 10 10/25/2021 4553.689941 4608.080078 4537.359863 ... $11,397.70 11000 1 0 $11,397.70
11 11 11/1/2021 4610.620117 4718.500000 4595.060059 ... $11,725.75 11100 1 0 $11,725.75
12 12 11/8/2021 4701.479980 4714.919922 4630.859863 ... $11,789.11 11200 1 0 $11,789.11
13 13 11/15/2021 4689.299805 4717.750000 4672.779785 ... $11,927.15 11300 1 0 $11,927.15
14 14 11/22/2021 4712.000000 4743.830078 4585.430176 ... $11,764.79 11400 1 0 $11,764.79
15 15 11/29/2021 4628.750000 4672.950195 4495.120117 ... $11,720.92 11500 -1 100 $11,720.92
16 16 12/6/2021 4548.370117 4713.569824 4540.509766 ... $12,269.23 11600 -1 100 $11,820.92
17 17 12/13/2021 4710.299805 4731.990234 4600.220215 ... $12,131.29 11700 1 0 $11,920.92
18 18 12/20/2021 4587.899902 4740.740234 4531.100098 ... $12,507.36 11800 1 0 $12,292.19
19 19 12/27/2021 4733.990234 4808.930176 4733.990234 ... $12,714.25 11900 1 0 $12,497.25
20 20 1/3/2022 4778.140137 4818.620117 4662.740234 ... $12,576.44 12000 1 0 $12,363.49
21 21 1/10/2022 4655.339844 4748.830078 4582.240234 ... $12,638.31 12100 1 0 $12,426.01
22 22 1/17/2022 4632.240234 4632.240234 4395.339844 ... $12,020.29 12200 1 0 $11,820.05
23 23 1/24/2022 4356.319824 4453.229980 4222.620117 ... $12,212.97 12300 -1 100 $12,011.19
24 24 1/31/2022 4431.790039 4595.310059 4414.020020 ... $12,502.23 12400 -1 100 $12,111.19
25 25 2/7/2022 4505.750000 4590.029785 4401.410156 ... $12,374.75 12500 1 0 $12,211.19
26 26 2/14/2022 4412.609863 4489.549805 4327.220215 ... $12,279.35 12600 1 0 $12,118.38
27 27 2/21/2022 4332.740234 4385.339844 4114.649902 ... $12,480.38 12700 1 0 $12,318.08
28 28 2/28/2022 4354.169922 4416.779785 4279.540039 ... $12,421.61 12800 1 0 $12,261.37
29 29 3/7/2022 4327.009766 4327.009766 4157.870117 ... $12,164.19 12900 -1 100 $12,008.56
30 30 3/14/2022 4202.750000 4465.399902 4161.720215 ... $13,012.99 13000 -1 100 $12,108.56
31 31 3/21/2022 4462.399902 4546.029785 4424.299805 ... $13,346.07 13100 1 0 $12,208.56
32 32 3/28/2022 4541.089844 4637.299805 4507.569824 ... $13,454.30 13200 1 0 $12,316.09
33 33 4/4/2022 4547.970215 4593.450195 4450.040039 ... $13,383.88 13300 1 0 $12,260.08
34 34 4/11/2022 4462.640137 4471.000000 4381.339844 ... $13,198.53 13400 1 0 $12,098.70
35 35 4/18/2022 4385.629883 4512.939941 4267.620117 ... $12,935.53 13500 -1 100 $11,865.95
36 36 4/25/2022 4255.339844 4308.450195 4124.279785 ... $12,612.05 13600 -1 100 $11,965.95
37 37 5/2/2022 4130.609863 4307.660156 4062.510010 ... $12,685.83 13700 -1 100 $12,065.95
38 38 5/9/2022 4081.270020 4081.270020 3858.870117 ... $12,479.86 13800 -1 100 $12,165.95
39 39 5/16/2022 4013.020020 4090.719971 3810.320068 ... $12,199.84 13900 -1 100 $12,265.95
40 40 5/23/2022 3919.419922 4158.490234 3875.129883 ... $13,103.12 14000 -1 100 $12,365.95
41 41 5/30/2022 4151.089844 4177.509766 4073.850098 ... $13,046.51 14100 1 0 $12,465.95
42 42 6/6/2022 4134.720215 4168.779785 3900.159912 ... $12,487.03 14200 1 0 $11,935.81
43 43 6/13/2022 3838.149902 3838.149902 3636.870117 ... $11,863.52 14300 -1 100 $11,344.24
44 44 6/20/2022 3715.310059 3913.649902 3715.310059 ... $12,728.31 14400 -1 100 $11,444.24
45 45 6/27/2022 3920.760010 3945.860107 3738.669922 ... $12,547.14 14500 -1 100 $11,544.24
46 46 7/4/2022 3792.610107 3918.500000 3742.060059 ... $12,890.03 14600 -1 100 $11,644.24
47 47 7/11/2022 3880.939941 3880.939941 3721.560059 ... $12,870.29 14700 -1 100 $11,744.24
48 48 7/18/2022 3883.790039 4012.439941 3818.629883 ... $13,298.35 14800 -1 100 $11,844.24
49 49 7/25/2022 3965.719971 4140.149902 3910.739990 ... $13,964.51 14900 1 0 $11,944.24
50 50 8/1/2022 4112.379883 4167.660156 4079.810059 ... $14,114.88 15000 1 0 $12,087.33
51 51 8/8/2022 4155.930176 4280.470215 4112.089844 ... $14,674.44 15100 1 0 $12,580.87
52 52 8/15/2022 4269.370117 4325.279785 4253.080078 ... $14,786.75 15200 1 0 $12,691.42
53 53 8/19/2022 4266.310059 4266.310059 4218.700195 ... $14,696.00 15300 1 0 $12,627.70
Python Code: Python 代码：
from ctypes.wintypes import VARIANT_BOOL
from xml.dom.expatbuilder import FilterVisibilityController
import ccxt
from matplotlib import pyplot as plt
import config
import schedule
import pandas as pd
import pandas_ta as ta
pd.set_option('display.max_rows', None)
#pd.set_option('display.max_columns', None)
import warnings
warnings.filterwarnings('ignore')
import numpy as np
from datetime import datetime
import time
import yfinance as yf
ticker = yf.Ticker('^GSPC')
df = ticker.history(period="1y", interval="1wk")
df.reset_index(inplace=True)
df.rename(columns = {'Date':'timestamp'}, inplace = True)
#df.drop(columns ={'Open', 'High', 'Low', 'Volume'}, inplace=True, axis=1)
df.drop(columns ={'Dividends', 'Stock Splits'}, inplace=True, axis=1)
# df['Close'].ffill(axis = 0, inplace = True)
invest = 10000
weekly = 100
fee = .15/100
fees = 1-fee
df.loc[df.index == 0, 'rate'] = 1
df.loc[df.index > 0, 'rate'] = (df['Close'] / df['Close'].shift(1))-1
df.loc[df.index == 0, 'account bal'] = invest
for i in range(1, len(df)):
df.loc[i, 'account bal'] = (df.loc[i-1, 'account bal'] * (1 + df.loc[i, 'rate'])) + weekly
df['invested'] = (df.index*weekly)+invest
#Supertrend
ATR = 10
Mult = 1.0
ST = ta.supertrend(df['High'], df['Low'], df['Close'], ATR, Mult)
df[f'ST_{ATR}_{Mult}'] = ST[f'SUPERTd_{ATR}_{Mult}']
df[f'ST_{ATR}_{Mult}'] = df[f'ST_{ATR}_{Mult}'].shift(1).fillna(1)
df.loc[df[f'ST_{ATR}_{Mult}'] == 1, 'if trend'] = 0
df.loc[df[f'ST_{ATR}_{Mult}'] == -1, 'if trend'] = weekly
# df.loc[df.index == 0, 'trend bal'] = invest
# for i in range(1, len(df)):
# np.where(df.loc[df[f'ST_{ATR}_{Mult}'] == 1, 'trend bal'], (df.loc[i-1, 'trend bal'] * (1 + df.loc[i, 'rate'])) + weekly, df.loc[i-i, 'trend bal'] + df['if trend'])
# df.loc[df.index == 0, 'trend bal'] = invest
# for i in range(1, len(df)):
# if df[f'ST_{ATR}_{Mult}'] == 1:
# df.loc[i, 'trend bal'] = (df.loc[i-1, 'trend bal'] * (1 + df.loc[i, 'rate'])) + weekly
# else:
# df.loc[i, 'trend bal'] = df.loc[i-i, 'trend bal'] + df['if trend']
# for i in range(1, len(df)):
# df.loc[df[f'ST_{ATR}_{Mult}'].shift(1) == 1, 'trend bal'] = (df.loc[i-1, 'trend bal'] * (1 + df.loc[i, 'rate'])) + weekly
# df.loc[df[f'ST_{ATR}_{Mult}'].shift(1) == -1, 'trend bal'] = df.loc[i-i, 'trend bal'] + df['if trend']
#df.to_csv('GSPC.csv',index=False,mode='a')
# plt.plot(df['timestamp'], df['account bal'])
# plt.plot(df['timestamp'], df['invested'])
# plt.plot(df['timestamp'], df['close'])
# plt.show()
print(df)
What some errors looks like:一些错误是什么样的：
np.where(df.loc[df[f'ST_{ATR}_{Mult}'] == 1, 'trend bal'], (df.loc[i-1, 'trend bal'] * (1 + df.loc[i, 'rate'])) + weekly, df.loc[i-i, 'trend bal'] + df['if trend'])
File "<__array_function__ internals>", line 180, in where
ValueError: operands could not be broadcast together with shapes (36,) () (54,)
Another error:另一个错误：
line 1535, in __nonzero__
raise ValueError(
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
No error but not the correct amounts:没有错误，但金额不正确：
df['trend bal'] = 0
for i in range(1, len(df)):
df.loc[df[f'ST_{ATR}_{Mult}'].shift(1) == 1, 'trend bal'] = (df.loc[i-1, 'trend bal'] * (1 + df.loc[i, 'rate'])) + weekly
df.loc[df[f'ST_{ATR}_{Mult}'].shift(1) == -1, 'trend bal'] = df.loc[i-i, 'trend bal'] + df['if trend']
See photo of screenshot of excel formula: excel spreadsheet请参阅 excel 公式的屏幕截图照片： excel 电子表格
*** Made correct calculations thanks to Ingwersen_erik: *** 感谢 Ingwersen_erik 进行了正确的计算：
from re import X
import pandas as pd
import pandas_ta as ta
import numpy as np
pd.set_option('display.max_rows', None)
df = pd.read_csv('etcusd.csv')
invest = 10000
weekly = 100
fee = .15/100
fees = 1-fee
df.loc[df.index == 0, 'rate'] = 1
df.loc[df.index > 0, 'rate'] = (df['Close'] / df['Close'].shift(1))-1
df.loc[df.index == 0, 'account bal'] = invest
for i in range(1, len(df)):
df.loc[i, 'account bal'] = (df.loc[i-1, 'account bal'] * (1 + df.loc[i, 'rate'])) + weekly
df['invested'] = (df.index*weekly)+invest
MDD = ((df['account bal']-df['account bal'].max()) / df['account bal'].max()).min()
#Supertrend
ATR = 10
Mult = 1.0
ST = ta.supertrend(df['High'], df['Low'], df['Close'], ATR, Mult)
df[f'ST_{ATR}_{Mult}'] = ST[f'SUPERTd_{ATR}_{Mult}']
df[f'ST_{ATR}_{Mult}'] = df[f'ST_{ATR}_{Mult}'].shift(1).fillna(1)
df.loc[df.index == 0, "trend bal"] = invest
for index, row in df.iloc[1:].iterrows():
row['trend bal'] = np.where(
df.loc[index - 1, f'ST_{ATR}_{Mult}'] == 1,
(df.loc[index - 1, 'trend bal'] * (1 + row['rate'])) + weekly,
df.loc[index - 1, 'trend bal'] + weekly,
)
df.loc[df.index == index, 'trend bal'] = row['trend bal']
print(df)
1 个解决方案
解决方案1
1 已采纳 2022-08-20 09:44:04
Does this solve your problem?这能解决你的问题吗？
import time
import ccxt
import warnings
import pandas as pd
import pandas_ta as ta
import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
from ctypes.wintypes import VARIANT_BOOL
from xml.dom.expatbuilder import FilterVisibilityController
warnings.filterwarnings("ignore")
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
invest = 10_000
weekly = 100
fee = 0.15 / 100
fees = 1 - fee
ATR = 10
Mult = 1.0
ticker = yf.Ticker("^GSPC")
df = (
ticker.history(period="1y", interval="1wk")
.reset_index()
.rename(columns={"Date": "timestamp"})
.drop(columns={"Dividends", "Stock Splits"}, errors="ignore")
)
df.loc[df.index == 0, "rate"] = 1
df.loc[df.index > 0, "rate"] = (df["Close"] / df["Close"].shift(1)) - 1
df.loc[df.index == 0, "account bal"] = invest
df.loc[df.index == 0, "account bal"] = invest
for i in range(1, len(df)):
df.loc[i, "account bal"] = (
df.loc[i - 1, "account bal"] * (1 + df.loc[i, "rate"])
) + weekly
df["invested"] = (df.index * weekly) + invest
# Super-trend
ST = ta.supertrend(df["High"], df["Low"], df["Close"], ATR, Mult)
df[f"ST_{ATR}_{Mult}"] = ST[f"SUPERTd_{ATR}_{Mult}"]
df[f"ST_{ATR}_{Mult}"] = df[f"ST_{ATR}_{Mult}"].shift(1).fillna(1)
df.loc[df[f"ST_{ATR}_{Mult}"] == 1, "if trend"] = 0
df.loc[df[f"ST_{ATR}_{Mult}"] == -1, "if trend"] = weekly
df.loc[df.index == 0, "trend bal"] = invest
# === Potential correction to the np.where ==============================
for index, row in df.iloc[1:].iterrows():
row["trend bal"] = np.where(
row[f"ST_{ATR}_{Mult}"] == 1,
(df.loc[index - 1, "trend bal"] * (1 + row["rate"])) + weekly,
df.loc[index - 1, "trend bal"] + row["if trend"],
)
# NOTE: The original "otherwise" clause from `np.where` had the
# following value: `df.loc[index - index, "trend bal"] + ...`
# I assumed you meant `index -1`, instead of `index - index`,
# therefore the above code uses `index -1`. If you really meant
# `index - index`, please change the code accordingly.
df.loc[df.index == index, "trend bal"] = row["trend bal"]
df
Result:结果：
| timestamp时间戳 |
Open打开 |
High高的 |
Low低的 |
Close关 |
Volume体积 |
rate速度 |
account bal账户余额 |
invested投资 |
ST_10_1.0 ST_10_1.0 |
if trend如果趋势 |
trend bal趋势平衡 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-08-16 2021-08-16 |
4382.44 4382.44 |
4444.35 4444.35 |
4367.73 4367.73 |
4441.67 4441.67 |
5988610000 5988610000 |
1 1 |
10000 10000 |
10000 10000 |
1 1 |
0 0 |
10000 10000 |
| 2021-08-23 2021-08-23 |
4450.29 4450.29 |
4513.33 4513.33 |
4450.29 4450.29 |
4509.37 4509.37 |
14124930000 14124930000 |
0.0152421 0.0152421 |
10252.4 10252.4 |
10100 10100 |
1 1 |
0 0 |
10252.4 10252.4 |
| 2021-08-30 2021-08-30 |
4513.76 4513.76 |
4545.85 4545.85 |
4513.76 4513.76 |
4535.43 4535.43 |
14256180000 14256180000 |
0.00577909 0.00577909 |
10411.7 10411.7 |
10200 10200 |
1 1 |
0 0 |
10411.7 10411.7 |
| 2021-09-06 2021-09-06 |
4535.38 4535.38 |
4535.38 4535.38 |
4457.66 4457.66 |
4458.58 4458.58 |
11793790000 11793790000 |
-0.0169444 -0.0169444 |
10335.3 10335.3 |
10300 10300 |
1 1 |
0 0 |
10335.3 10335.3 |
| 2021-09-13 2021-09-13 |
4474.81 4474.81 |
4492.99 4492.99 |
4427.76 4427.76 |
4432.99 4432.99 |
17763120000 17763120000 |
-0.00573946 -0.00573946 |
10375.9 10375.9 |
10400 10400 |
1 1 |
0 0 |
10375.9 10375.9 |
| 2021-09-20 2021-09-20 |
4402.95 4402.95 |
4465.4 4465.4 |
4305.91 4305.91 |
4455.48 4455.48 |
15697030000 15697030000 |
0.00507327 0.00507327 |
10528.6 10528.6 |
10500 10500 |
1 1 |
0 0 |
10528.6 10528.6 |
| 2021-09-27 2021-09-27 |
4442.12 4442.12 |
4457.3 4457.3 |
4288.52 4288.52 |
4357.04 4357.04 |
15555390000 15555390000 |
-0.0220941 -0.0220941 |
10396 10396 |
10600 10600 |
1 1 |
0 0 |
10396 10396 |
| 2021-10-04 2021-10-04 |
4348.84 4348.84 |
4429.97 4429.97 |
4278.94 4278.94 |
4391.34 4391.34 |
14795520000 14795520000 |
0.00787227 0.00787227 |
10577.8 10577.8 |
10700 10700 |
1 1 |
0 0 |
10577.8 10577.8 |
| 2021-10-11 2021-10-11 |
4385.44 4385.44 |
4475.82 4475.82 |
4329.92 4329.92 |
4471.37 4471.37 |
13758090000 13758090000 |
0.0182246 0.0182246 |
10870.6 10870.6 |
10800 10800 |
1 1 |
0 0 |
10870.6 10870.6 |
| 2021-10-18 2021-10-18 |
4463.72 4463.72 |
4559.67 4559.67 |
4447.47 4447.47 |
4544.9 4544.9 |
13966070000 13966070000 |
0.0164446 0.0164446 |
11149.3 11149.3 |
10900 10900 |
1 1 |
0 0 |
11149.3 11149.3 |
| 2021-10-25 2021-10-25 |
4553.69 4553.69 |
4608.08 4608.08 |
4537.36 4537.36 |
4605.38 4605.38 |
16206040000 16206040000 |
0.0133072 0.0133072 |
11397.7 11397.7 |
11000 11000 |
1 1 |
0 0 |
11397.7 11397.7 |
| 2021-11-01 2021-11-01 |
4610.62 4610.62 |
4718.5 4718.5 |
4595.06 4595.06 |
4697.53 4697.53 |
16397220000 16397220000 |
0.0200092 0.0200092 |
11725.8 11725.8 |
11100 11100 |
1 1 |
0 0 |
11725.8 11725.8 |
| 2021-11-08 2021-11-08 |
4701.48 4701.48 |
4714.92 4714.92 |
4630.86 4630.86 |
4682.85 4682.85 |
15646510000 15646510000 |
-0.00312498 -0.00312498 |
11789.1 11789.1 |
11200 11200 |
1 1 |
0 0 |
11789.1 11789.1 |
| 2021-11-15 2021-11-15 |
4689.3 4689.3 |
4717.75 4717.75 |
4672.78 4672.78 |
4697.96 4697.96 |
15279660000 15279660000 |
0.00322664 0.00322664 |
11927.2 11927.2 |
11300 11300 |
1 1 |
0 0 |
11927.2 11927.2 |
| 2021-11-22 2021-11-22 |
4712 4712 |
4743.83 4743.83 |
4585.43 4585.43 |
4594.62 4594.62 |
11775840000 11775840000 |
-0.0219967 -0.0219967 |
11764.8 11764.8 |
11400 11400 |
1 1 |
0 0 |
11764.8 11764.8 |
| 2021-11-29 2021-11-29 |
4628.75 4628.75 |
4672.95 4672.95 |
4495.12 4495.12 |
4538.43 4538.43 |
20242840000 20242840000 |
-0.0122295 -0.0122295 |
11720.9 11720.9 |
11500 11500 |
-1 -1 |
100 100 |
11864.8 11864.8 |
| 2021-12-06 2021-12-06 |
4548.37 4548.37 |
4713.57 4713.57 |
4540.51 4540.51 |
4712.02 4712.02 |
15411530000 15411530000 |
0.0382489 0.0382489 |
12269.2 12269.2 |
11600 11600 |
-1 -1 |
100 100 |
11964.8 11964.8 |
| 2021-12-13 2021-12-13 |
4710.3 4710.3 |
4731.99 4731.99 |
4600.22 4600.22 |
4620.64 4620.64 |
19184960000 19184960000 |
-0.0193929 -0.0193929 |
12131.3 12131.3 |
11700 11700 |
1 1 |
0 0 |
11832.8 11832.8 |
| 2021-12-20 2021-12-20 |
4587.9 4587.9 |
4740.74 4740.74 |
4531.1 4531.1 |
4725.79 4725.79 |
10594350000 10594350000 |
0.0227566 0.0227566 |
12507.4 12507.4 |
11800 11800 |
1 1 |
0 0 |
12202 12202 |
| 2021-12-27 2021-12-27 |
4733.99 4733.99 |
4808.93 4808.93 |
4733.99 4733.99 |
4766.18 4766.18 |
11687720000 11687720000 |
0.00854675 0.00854675 |
12714.3 12714.3 |
11900 11900 |
1 1 |
0 0 |
12406.3 12406.3 |
| 2022-01-03 2022-01-03 |
4778.14 4778.14 |
4818.62 4818.62 |
4662.74 4662.74 |
4677.03 4677.03 |
16800900000 16800900000 |
-0.0187048 -0.0187048 |
12576.4 12576.4 |
12000 12000 |
1 1 |
0 0 |
12274.3 12274.3 |
| 2022-01-10 2022-01-10 |
4655.34 4655.34 |
4748.83 4748.83 |
4582.24 4582.24 |
4662.85 4662.85 |
17126800000 17126800000 |
-0.00303177 -0.00303177 |
12638.3 12638.3 |
12100 12100 |
1 1 |
0 0 |
12337.1 12337.1 |
| 2022-01-17 2022-01-17 |
4632.24 4632.24 |
4632.24 4632.24 |
4395.34 4395.34 |
4397.94 4397.94 |
14131200000 14131200000 |
-0.0568129 -0.0568129 |
12020.3 12020.3 |
12200 12200 |
1 1 |
0 0 |
11736.1 11736.1 |
| 2022-01-24 2022-01-24 |
4356.32 4356.32 |
4453.23 4453.23 |
4222.62 4222.62 |
4431.85 4431.85 |
21218590000 21218590000 |
0.00771046 0.00771046 |
12213 12213 |
12300 12300 |
-1 -1 |
100 100 |
11836.1 11836.1 |
| 2022-01-31 2022-01-31 |
4431.79 4431.79 |
4595.31 4595.31 |
4414.02 4414.02 |
4500.53 4500.53 |
18846100000 18846100000 |
0.0154968 0.0154968 |
12502.2 12502.2 |
12400 12400 |
-1 -1 |
100 100 |
11936.1 11936.1 |
| 2022-02-07 2022-02-07 |
4505.75 4505.75 |
4590.03 4590.03 |
4401.41 4401.41 |
4418.64 4418.64 |
19119200000 19119200000 |
-0.0181956 -0.0181956 |
12374.7 12374.7 |
12500 12500 |
1 1 |
0 0 |
11819 11819 |
| 2022-02-14 2022-02-14 |
4412.61 4412.61 |
4489.55 4489.55 |
4327.22 4327.22 |
4348.87 4348.87 |
17775970000 17775970000 |
-0.0157899 -0.0157899 |
12279.4 12279.4 |
12600 12600 |
1 1 |
0 0 |
11732.3 11732.3 |
| 2022-02-21 2022-02-21 |
4332.74 4332.74 |
4385.34 4385.34 |
4114.65 4114.65 |
4384.65 4384.65 |
16834460000 16834460000 |
0.00822737 0.00822737 |
12480.4 12480.4 |
12700 12700 |
1 1 |
0 0 |
11928.9 11928.9 |
| 2022-02-28 2022-02-28 |
4354.17 4354.17 |
4416.78 4416.78 |
4279.54 4279.54 |
4328.87 4328.87 |
22302830000 22302830000 |
-0.0127216 -0.0127216 |
12421.6 12421.6 |
12800 12800 |
1 1 |
0 0 |
11877.1 11877.1 |
| 2022-03-07 2022-03-07 |
4327.01 4327.01 |
4327.01 4327.01 |
4157.87 4157.87 |
4204.31 4204.31 |
23849630000 23849630000 |
-0.0287743 -0.0287743 |
12164.2 12164.2 |
12900 12900 |
-1 -1 |
100 100 |
11977.1 11977.1 |
| 2022-03-14 2022-03-14 |
4202.75 4202.75 |
4465.4 4465.4 |
4161.72 4161.72 |
4463.12 4463.12 |
24946690000 24946690000 |
0.0615583 0.0615583 |
13013 13013 |
13000 13000 |
-1 -1 |
100 100 |
12077.1 12077.1 |
| 2022-03-21 2022-03-21 |
4462.4 4462.4 |
4546.03 4546.03 |
4424.3 4424.3 |
4543.06 4543.06 |
19089240000 19089240000 |
0.0179112 0.0179112 |
13346.1 13346.1 |
13100 13100 |
1 1 |
0 0 |
12393.4 12393.4 |
| 2022-03-28 2022-03-28 |
4541.09 4541.09 |
4637.3 4637.3 |
4507.57 4507.57 |
4545.86 4545.86 |
19212230000 19212230000 |
0.000616282 0.000616282 |
13454.3 13454.3 |
13200 13200 |
1 1 |
0 0 |
12501.1 12501.1 |
| 2022-04-04 2022-04-04 |
4547.97 4547.97 |
4593.45 4593.45 |
4450.04 4450.04 |
4488.28 4488.28 |
19383860000 19383860000 |
-0.0126665 -0.0126665 |
13383.9 13383.9 |
13300 13300 |
1 1 |
0 0 |
12442.7 12442.7 |
| 2022-04-11 2022-04-11 |
4462.64 4462.64 |
4471 4471 |
4381.34 4381.34 |
4392.59 4392.59 |
13812410000 13812410000 |
-0.02132 -0.02132 |
13198.5 13198.5 |
13400 13400 |
1 1 |
0 0 |
12277.4 12277.4 |
| 2022-04-18 2022-04-18 |
4385.63 4385.63 |
4512.94 4512.94 |
4267.62 4267.62 |
4271.78 4271.78 |
18149540000 18149540000 |
-0.0275032 -0.0275032 |
12935.5 12935.5 |
13500 13500 |
-1 -1 |
100 100 |
12377.4 12377.4 |
| 2022-04-25 2022-04-25 |
4255.34 4255.34 |
4308.45 4308.45 |
4124.28 4124.28 |
4131.93 4131.93 |
19610750000 19610750000 |
-0.032738 -0.032738 |
12612 12612 |
13600 13600 |
-1 -1 |
100 100 |
12477.4 12477.4 |
| 2022-05-02 2022-05-02 |
4130.61 4130.61 |
4307.66 4307.66 |
4062.51 4062.51 |
4123.34 4123.34 |
21039720000 21039720000 |
-0.00207901 -0.00207901 |
12685.8 12685.8 |
13700 13700 |
-1 -1 |
100 100 |
12577.4 12577.4 |
| 2022-05-09 2022-05-09 |
4081.27 4081.27 |
4081.27 4081.27 |
3858.87 3858.87 |
4023.89 4023.89 |
23166570000 23166570000 |
-0.0241188 -0.0241188 |
12479.9 12479.9 |
13800 13800 |
-1 -1 |
100 100 |
12677.4 12677.4 |
| 2022-05-16 2022-05-16 |
4013.02 4013.02 |
4090.72 4090.72 |
3810.32 3810.32 |
3901.36 3901.36 |
20590520000 20590520000 |
-0.0304506 -0.0304506 |
12199.8 12199.8 |
13900 13900 |
-1 -1 |
100 100 |
12777.4 12777.4 |
| 2022-05-23 2022-05-23 |
3919.42 3919.42 |
4158.49 4158.49 |
3875.13 3875.13 |
4158.24 4158.24 |
19139100000 19139100000 |
0.0658437 0.0658437 |
13103.1 13103.1 |
14000 14000 |
-1 -1 |
100 100 |
12877.4 12877.4 |
| 2022-05-30 2022-05-30 |
4151.09 4151.09 |
4177.51 4177.51 |
4073.85 4073.85 |
4108.54 4108.54 |
16049940000 16049940000 |
-0.0119522 -0.0119522 |
13046.5 13046.5 |
14100 14100 |
1 1 |
0 0 |
12823.5 12823.5 |
| 2022-06-06 2022-06-06 |
4134.72 4134.72 |
4168.78 4168.78 |
3900.16 3900.16 |
3900.86 3900.86 |
17547150000 17547150000 |
-0.0505484 -0.0505484 |
12487 12487 |
14200 14200 |
1 1 |
0 0 |
12275.3 12275.3 |
| 2022-06-13 2022-06-13 |
3838.15 3838.15 |
3838.15 3838.15 |
3636.87 3636.87 |
3674.84 3674.84 |
24639140000 24639140000 |
-0.0579411 -0.0579411 |
11863.5 11863.5 |
14300 14300 |
-1 -1 |
100 100 |
12375.3 12375.3 |
| 2022-06-20 2022-06-20 |
3715.31 3715.31 |
3913.65 3913.65 |
3715.31 3715.31 |
3911.74 3911.74 |
19287840000 19287840000 |
0.0644654 0.0644654 |
12728.3 12728.3 |
14400 14400 |
-1 -1 |
100 100 |
12475.3 12475.3 |
| 2022-06-27 2022-06-27 |
3920.76 3920.76 |
3945.86 3945.86 |
3738.67 3738.67 |
3825.33 3825.33 |
17735450000 17735450000 |
-0.0220899 -0.0220899 |
12547.1 12547.1 |
14500 14500 |
-1 -1 |
100 100 |
12575.3 12575.3 |
| 2022-07-04 2022-07-04 |
3792.61 3792.61 |
3918.5 3918.5 |
3742.06 3742.06 |
3899.38 3899.38 |
14223350000 14223350000 |
0.0193578 0.0193578 |
12890 12890 |
14600 14600 |
-1 -1 |
100 100 |
12675.3 12675.3 |
| 2022-07-11 2022-07-11 |
3880.94 3880.94 |
3880.94 3880.94 |
3721.56 3721.56 |
3863.16 3863.16 |
16313500000 16313500000 |
-0.00928865 -0.00928865 |
12870.3 12870.3 |
14700 14700 |
-1 -1 |
100 100 |
12775.3 12775.3 |
| 2022-07-18 2022-07-18 |
3883.79 3883.79 |
4012.44 4012.44 |
3818.63 3818.63 |
3961.63 3961.63 |
16859220000 16859220000 |
0.0254895 0.0254895 |
13298.4 13298.4 |
14800 14800 |
-1 -1 |
100 100 |
12875.3 12875.3 |
| 2022-07-25 2022-07-25 |
3965.72 3965.72 |
4140.15 4140.15 |
3910.74 3910.74 |
4130.29 4130.29 |
17356830000 17356830000 |
0.0425734 0.0425734 |
13964.5 13964.5 |
14900 14900 |
1 1 |
0 0 |
13523.5 13523.5 |
| 2022-08-01 2022-08-01 |
4112.38 4112.38 |
4167.66 4167.66 |
4079.81 4079.81 |
4145.19 4145.19 |
18072230000 18072230000 |
0.00360747 0.00360747 |
14114.9 14114.9 |
15000 15000 |
1 1 |
0 0 |
13672.3 13672.3 |
| 2022-08-08 2022-08-08 |
4155.93 4155.93 |
4280.47 4280.47 |
4112.09 4112.09 |
4280.15 4280.15 |
18117740000 18117740000 |
0.0325582 0.0325582 |
14674.4 14674.4 |
15100 15100 |
1 1 |
0 0 |
14217.4 14217.4 |
| 2022-08-15 2022-08-15 |
4269.37 4269.37 |
4325.28 4325.28 |
4218.7 4218.7 |
4228.48 4228.48 |
16255850000 16255850000 |
-0.012072 -0.012072 |
14597.3 14597.3 |
15200 15200 |
1 1 |
0 0 |
14145.8 14145.8 |
| 2022-08-19 2022-08-19 |
4266.31 4266.31 |
4266.31 4266.31 |
4218.7 4218.7 |
4228.48 4228.48 |
2045645000 2045645000 |
0 0 |
14697.3 14697.3 |
15300 15300 |
1 1 |
0 0 |
14245.8 14245.8 |
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