线性回归预测预测输入数据（测试数据），而不是测试结果
Linear regression prediction predicting input data (test data), and not test results
我正在尝试预测以下内容：
list( [ close price (current day) - open price (current day) ] )
使用以下内容作为输入：
list( [ open price (current day) - close price (yesterday) ] )
然而，我的 test_prediction 结果是对错误事物的预测。
sklearn 和 statsmodels 线性回归模型的预测显示输入数据 (test_data) 和 prediction results 之间的相关性约为 100%，而 prediction results 应该与 test_result.
相关
我做错了什么（或这里遗漏了什么），我该如何解决？
代码将生成 4 个图表，显示不同列表之间的相关性。
###### Working usable example and code below ######
import numpy as np
from plotly.offline import plot
import plotly.graph_objs as go
from sklearn import linear_model
import statsmodels.api as sm
def xy_corr( x, y, fname ):
trace1 = go.Scatter( x = x,
y = y,
mode = 'markers',
marker = dict( size = 6,
color = 'black',
),
showlegend = False
)
layout = go.Layout( title = fname )
fig = go.Figure( data = [trace1],
layout = layout
)
plot( fig, filename = fname + '.html' )
open_p = [23215, 23659, 23770, 23659, 23659, 23993, 23987, 23935, 24380, 24271, 24314, 24018, 23928, 23240, 24193, 23708, 23525, 23640, 23494, 23333, 23451, 23395, 23395, 23925, 23936, 24036, 24008, 24248, 24249, 24599, 24683, 24708, 24510, 24483, 24570, 24946, 25008, 24880, 24478, 24421, 24630, 24540, 24823, 25090, 24610, 24866, 24578, 24686, 24465, 24225, 24526, 24645, 24780, 24538, 24895, 24921, 24743, 25163, 25163, 25316, 25320, 25158, 25375, 25430, 25466, 25231, 25103, 25138, 25138, 25496, 25502, 25610, 25625, 25810, 25789, 25533, 25785, 25698, 25373, 25558, 25594, 25026, 24630, 24509, 24535, 24205, 24465, 23847, 24165, 23840, 24216, 24355, 24158, 23203, 23285, 23423, 23786, 23729, 23944, 23637]
close_p = [23656, 23758, 23663, 23659, 23989, 23978, 24142, 24152, 24279, 24271, 24393, 23942, 23640, 24102, 23710, 23708, 23705, 23693, 23561, 23441, 23395, 23395, 23990, 23900, 24158, 24188, 24241, 24248, 24699, 24678, 24715, 24523, 24486, 24483, 24947, 24904, 24923, 24478, 24434, 24421, 24409, 24705, 25047, 24642, 24875, 24866, 24698, 24463, 24262, 24396, 24633, 24645, 24528, 24895, 24895, 24839, 25178, 25163, 25315, 25323, 25149, 25387, 25375, 25469, 25231, 25073, 25138, 25138, 25448, 25611, 25705, 25623, 25813, 25798, 25560, 25518, 25743, 25305, 25654, 25579, 25315, 24783, 24508, 24532, 24208, 24176, 24047, 24148, 24165, 24159, 24286, 24249, 23635, 23128, 23438, 23869, 23420, 23756, 23705, 24018]
open_prev_close_diff = np.array( [ open_p[i] - close_p[i-1] for i in range( 1, len( open_p ) )] )[np.newaxis].T
open_current_close_diff = np.array( [close_p[i] - open_p[i] for i in range( 1, len( open_p ) )] )
train_data = open_prev_close_diff[ :80]
test_data = open_prev_close_diff[80:]
train_result = open_current_close_diff[ :80]
test_result = open_current_close_diff[80:]
regressor = linear_model.LinearRegression()
regressor.fit( train_data, train_result )
test_prediction = np.array( [int(i) for i in regressor.predict( test_data )] )
xy_corr( [int(i) for i in test_result], test_prediction, 'known_result_and_prediction_result_sklearn')
xy_corr( [int(i) for i in test_data], test_prediction, 'input_data_and_prediction_result_sklearn' )
olsmod = sm.OLS( train_result, train_data )
olsres = olsmod.fit()
test_prediction = np.array( [int(i) for i in olsres.predict( test_data )] )
xy_corr( [int(i) for i in test_result], test_prediction, 'known_result_and_prediction_result_smOLS')
xy_corr( [int(i) for i in test_data], test_prediction, 'input_data_and_prediction_result_smOLS' )
With a hope no one would consider this impolite and/or harmfull,
let me cite a lovely point of view on the underlying assumption from “Correlation does not imply Causation“: that many contemporary Quantitative Finance modellers neglect or abstract from:
对于任意两个相关事件，A和B,
可能存在以下关系：
A 导致 B； (直接因果关系)
B 导致 A； (反转因果关系)
A 和 B 是 共同原因 的结果，但不会相互导致；
A 导致 B，B 导致 A（双向或循环 因果关系）；
A 导致 C 导致 B（间接 因果关系）；
A和B之间没有联系；相关性是 coincidence.
因此无法得出关于存在或方向的结论因果关系仅来自事实 A 和 B 相关。
更新：
LinearRegressor()除了一条线之外没有产生任何其他东西——也就是说，它的每一个预测都确实在很线的模特。因此，一半的图 显然是这种预测器必须做的 。
Q.E.D.
尽管 可观察到的现实，但另一半没有显示任何其他内容，但线性模型采用的过度简化程度 。
当然，实际经历的行为是不是线性的（但不要责怪预测器“无法拟合”，它是有责任制定一个 MSE-minimiser 驱动的线性模型，它在 DataSET 的训练部分找不到更好的线性拟合，如果它被训练在 y = x^2 合成 DataSET （ 中，人们对抛物线形状有先验知识 ) , again 它只会产生一个线性模型，在训练过程中至少有 MSE DataSET 和 的部分分数我们都提前确定，因此任何一行都会产生完全有缺陷的 预测 OoS，但不是因为它不能很好地工作，而是由于外部灌输的主要废话尝试在（已知的二次）上下文中使用线性模型预测器，它不遵循（已知的）现实） .
数据集：
作为一个基本的定量观点，比严格的Kolmogorov-Smirnov检验简单得多，对于未表达的假设，
检查日内差距( Open[i] - Close[i-1] )[ 75% ]的负差百分比，来自相当浅的DataSET，只有100个样本
反对
当日蜡烛体( Close[i] - Open[i] )与相当浅的DataSET仅[ 55% ]的负差共 100 个样本
无论如何，训练少至 80 天的样本很难 很好地泛化 即使是在更好的工程预测模型上，而且应该不仅关注更好的泛化能力，还有避免季节性偏差等
To have some idea where the ML goes into this field, my best preforming AI/ML-models have about 0k3 features ( many of 'em highly non-linear synthetic features ) and get intensively trained accross 30k+ DataSETs with carefully carving out their risk to overfitting and searching vast space of the learner-engines' hyperparameter StateSPACE
|
|>>> QuantFX.get_LDF_GDF_fromGivenRANGE( [ open_PRICE[i] - close_PRICE[i-1] for i in range( 1, len( close_PRICE ) ) ], nBINs_ = 31, aPrefixTEXT_ = "" )
0: ~ -432.00 LDF = 1 |____ 1.0 % _||____ 1 %
1: ~ -408.10 LDF = 1 |____ 1.0 % _||____ 2 %
2: ~ -384.19 LDF = 1 |____ 1.0 % _||____ 3 %
3: ~ -360.29 LDF = 0 |____ 0.0 % _||____ 3 %
4: ~ -336.39 LDF = 1 |____ 1.0 % _||____ 4 %
5: ~ -312.48 LDF = 1 |____ 1.0 % _||____ 5 %
6: ~ -288.58 LDF = 1 |____ 1.0 % _||____ 6 %
7: ~ -264.68 LDF = 0 |____ 0.0 % _||____ 6 %
8: ~ -240.77 LDF = 1 |____ 1.0 % _||____ 7 %
9: ~ -216.87 LDF = 3 |____ 3.0 % _||___ 10 %
10: ~ -192.97 LDF = 2 |____ 2.0 % _||___ 12 %
11: ~ -169.06 LDF = 1 |____ 1.0 % _||___ 13 %
12: ~ -145.16 LDF = 1 |____ 1.0 % _||___ 14 %
13: ~ -121.26 LDF = 2 |____ 2.0 % _||___ 16 %
14: ~ -97.35 LDF = 5 |____ 5.1 % _||___ 21 %
15: ~ -73.45 LDF = 3 |____ 3.0 % _||___ 24 %
16: ~ -49.55 LDF = 5 |____ 5.1 % _||___ 29 %
17: ~ -25.65 LDF = 18 |___ 18.2 % _||___ 47 %
18: ~ -1.74 LDF = 28 |___ 28.3 % _||___ 75 %
19: ~ 22.16 LDF = 5 |____ 5.1 % _||___ 80 %
20: ~ 46.06 LDF = 5 |____ 5.1 % _||___ 85 %
21: ~ 69.97 LDF = 2 |____ 2.0 % _||___ 87 %
22: ~ 93.87 LDF = 1 |____ 1.0 % _||___ 88 %
23: ~ 117.77 LDF = 4 |____ 4.0 % _||___ 92 %
24: ~ 141.68 LDF = 1 |____ 1.0 % _||___ 93 %
25: ~ 165.58 LDF = 1 |____ 1.0 % _||___ 94 %
26: ~ 189.48 LDF = 1 |____ 1.0 % _||___ 95 %
27: ~ 213.39 LDF = 1 |____ 1.0 % _||___ 96 %
28: ~ 237.29 LDF = 0 |____ 0.0 % _||___ 96 %
29: ~ 261.19 LDF = 1 |____ 1.0 % _||___ 97 %
30: ~ 285.10 LDF = 2 |____ 2.0 % _||__ 100 %
+0:00:06.234000
|
|
|>>> QuantFX.get_LDF_GDF_fromGivenRANGE( [ close_PRICE[i] - open_PRICE[i] for i in range( 1, len( close_PRICE ) ) ], nBINs_ = 31, aPrefixTEXT_ = "" )
0: ~ -523.00 LDF = 2 |____ 2.0 % _||____ 2 %
1: ~ -478.32 LDF = 1 |____ 1.0 % _||____ 3 %
2: ~ -433.65 LDF = 3 |____ 3.0 % _||____ 6 %
3: ~ -388.97 LDF = 1 |____ 1.0 % _||____ 7 %
4: ~ -344.29 LDF = 1 |____ 1.0 % _||____ 8 %
5: ~ -299.61 LDF = 2 |____ 2.0 % _||___ 10 %
6: ~ -254.94 LDF = 7 |____ 7.1 % _||___ 17 %
7: ~ -210.26 LDF = 3 |____ 3.0 % _||___ 20 %
8: ~ -165.58 LDF = 2 |____ 2.0 % _||___ 22 %
9: ~ -120.90 LDF = 5 |____ 5.1 % _||___ 27 %
10: ~ -76.23 LDF = 6 |____ 6.1 % _||___ 33 %
11: ~ -31.55 LDF = 22 |___ 22.2 % _||___ 55 %
12: ~ 13.13 LDF = 7 |____ 7.1 % _||___ 62 %
13: ~ 57.81 LDF = 5 |____ 5.1 % _||___ 67 %
14: ~ 102.48 LDF = 4 |____ 4.0 % _||___ 71 %
15: ~ 147.16 LDF = 8 |____ 8.1 % _||___ 79 %
16: ~ 191.84 LDF = 6 |____ 6.1 % _||___ 85 %
17: ~ 236.52 LDF = 2 |____ 2.0 % _||___ 87 %
18: ~ 281.19 LDF = 3 |____ 3.0 % _||___ 90 %
19: ~ 325.87 LDF = 2 |____ 2.0 % _||___ 92 %
20: ~ 370.55 LDF = 2 |____ 2.0 % _||___ 94 %
21: ~ 415.23 LDF = 3 |____ 3.0 % _||___ 97 %
22: ~ 459.90 LDF = 0 |____ 0.0 % _||___ 97 %
23: ~ 504.58 LDF = 0 |____ 0.0 % _||___ 97 %
24: ~ 549.26 LDF = 0 |____ 0.0 % _||___ 97 %
25: ~ 593.94 LDF = 1 |____ 1.0 % _||___ 98 %
26: ~ 638.61 LDF = 0 |____ 0.0 % _||___ 98 %
27: ~ 683.29 LDF = 0 |____ 0.0 % _||___ 98 %
28: ~ 727.97 LDF = 0 |____ 0.0 % _||___ 98 %
29: ~ 772.65 LDF = 0 |____ 0.0 % _||___ 98 %
30: ~ 817.32 LDF = 1 |____ 1.0 % _||__ 100 %
+0:01:13.172000
我正在尝试预测以下内容：
list( [close price (current day)-open price (current day)] )
使用以下内容作为输入：
list( [open price (current day)-close price (yesterday)] )
然而，我的 test_prediction 结果是对错误事物的预测。
sklearn 和 statsmodels 线性回归模型的预测显示输入数据 (test_data) 和 prediction results 之间的相关性约为 100%，而 prediction results 应该与 test_result.
我做错了什么（或这里遗漏了什么），我该如何解决？
###### Working usable example and code below ######
import numpy as np
from plotly.offline import plot
import plotly.graph_objs as go
from sklearn import linear_model
import statsmodels.api as sm
def xy_corr( x, y, fname ):
trace1 = go.Scatter( x = x,
y = y,
mode = 'markers',
marker = dict( size = 6,
color = 'black',
),
showlegend = False
)
layout = go.Layout( title = fname )
fig = go.Figure( data = [trace1],
layout = layout
)
plot( fig, filename = fname + '.html' )
open_p = [23215, 23659, 23770, 23659, 23659, 23993, 23987, 23935, 24380, 24271, 24314, 24018, 23928, 23240, 24193, 23708, 23525, 23640, 23494, 23333, 23451, 23395, 23395, 23925, 23936, 24036, 24008, 24248, 24249, 24599, 24683, 24708, 24510, 24483, 24570, 24946, 25008, 24880, 24478, 24421, 24630, 24540, 24823, 25090, 24610, 24866, 24578, 24686, 24465, 24225, 24526, 24645, 24780, 24538, 24895, 24921, 24743, 25163, 25163, 25316, 25320, 25158, 25375, 25430, 25466, 25231, 25103, 25138, 25138, 25496, 25502, 25610, 25625, 25810, 25789, 25533, 25785, 25698, 25373, 25558, 25594, 25026, 24630, 24509, 24535, 24205, 24465, 23847, 24165, 23840, 24216, 24355, 24158, 23203, 23285, 23423, 23786, 23729, 23944, 23637]
close_p = [23656, 23758, 23663, 23659, 23989, 23978, 24142, 24152, 24279, 24271, 24393, 23942, 23640, 24102, 23710, 23708, 23705, 23693, 23561, 23441, 23395, 23395, 23990, 23900, 24158, 24188, 24241, 24248, 24699, 24678, 24715, 24523, 24486, 24483, 24947, 24904, 24923, 24478, 24434, 24421, 24409, 24705, 25047, 24642, 24875, 24866, 24698, 24463, 24262, 24396, 24633, 24645, 24528, 24895, 24895, 24839, 25178, 25163, 25315, 25323, 25149, 25387, 25375, 25469, 25231, 25073, 25138, 25138, 25448, 25611, 25705, 25623, 25813, 25798, 25560, 25518, 25743, 25305, 25654, 25579, 25315, 24783, 24508, 24532, 24208, 24176, 24047, 24148, 24165, 24159, 24286, 24249, 23635, 23128, 23438, 23869, 23420, 23756, 23705, 24018]
open_prev_close_diff = np.array( [ open_p[i] - close_p[i-1] for i in range( 1, len( open_p ) )] )[np.newaxis].T
open_current_close_diff = np.array( [close_p[i] - open_p[i] for i in range( 1, len( open_p ) )] )
train_data = open_prev_close_diff[ :80]
test_data = open_prev_close_diff[80:]
train_result = open_current_close_diff[ :80]
test_result = open_current_close_diff[80:]
regressor = linear_model.LinearRegression()
regressor.fit( train_data, train_result )
test_prediction = np.array( [int(i) for i in regressor.predict( test_data )] )
xy_corr( [int(i) for i in test_result], test_prediction, 'known_result_and_prediction_result_sklearn')
xy_corr( [int(i) for i in test_data], test_prediction, 'input_data_and_prediction_result_sklearn' )
olsmod = sm.OLS( train_result, train_data )
olsres = olsmod.fit()
test_prediction = np.array( [int(i) for i in olsres.predict( test_data )] )
xy_corr( [int(i) for i in test_result], test_prediction, 'known_result_and_prediction_result_smOLS')
xy_corr( [int(i) for i in test_data], test_prediction, 'input_data_and_prediction_result_smOLS' )
With a hope no one would consider this impolite and/or harmfull,
let me cite a lovely point of view on the underlying assumption from “Correlation does not imply Causation“: that many contemporary Quantitative Finance modellers neglect or abstract from:
对于任意两个相关事件，A和B,
可能存在以下关系：
A 导致 B； (直接因果关系)
B 导致 A； (反转因果关系)
A 和 B 是 共同原因 的结果，但不会相互导致；
A 导致 B，B 导致 A（双向或循环 因果关系）；
A 导致 C 导致 B（间接 因果关系）；
A和B之间没有联系；相关性是 coincidence.
因此无法得出关于存在或方向的结论因果关系仅来自事实 A 和 B 相关。
更新：
LinearRegressor()除了一条线之外没有产生任何其他东西——也就是说，它的每一个预测都确实在很线的模特。因此，一半的图 显然是这种预测器必须做的 。
Q.E.D.
尽管 可观察到的现实，但另一半没有显示任何其他内容，但线性模型采用的过度简化程度 。
当然，实际经历的行为是不是线性的（但不要责怪预测器“无法拟合”，它是有责任制定一个 MSE-minimiser 驱动的线性模型，它在 DataSET 的训练部分找不到更好的线性拟合，如果它被训练在 y = x^2 合成 DataSET （ 中，人们对抛物线形状有先验知识 ) , again 它只会产生一个线性模型，在训练过程中至少有 MSE DataSET 和 的部分分数我们都提前确定，因此任何一行都会产生完全有缺陷的 预测 OoS，但不是因为它不能很好地工作，而是由于外部灌输的主要废话尝试在（已知的二次）上下文中使用线性模型预测器，它不遵循（已知的）现实） .
数据集：
作为一个基本的定量观点，比严格的Kolmogorov-Smirnov检验简单得多，对于未表达的假设，
检查日内差距( Open[i] - Close[i-1] )[ 75% ]的负差百分比，来自相当浅的DataSET，只有100个样本
反对
当日蜡烛体( Close[i] - Open[i] )与相当浅的DataSET仅[ 55% ]的负差共 100 个样本
无论如何，训练少至 80 天的样本很难 很好地泛化 即使是在更好的工程预测模型上，而且应该不仅关注更好的泛化能力，还有避免季节性偏差等
To have some idea where the ML goes into this field, my best preforming AI/ML-models have about
0k3features ( many of 'em highly non-linear synthetic features ) and get intensively trained accross30k+ DataSETswith carefully carving out their risk to overfitting and searching vast space of the learner-engines' hyperparameterStateSPACE
|
|>>> QuantFX.get_LDF_GDF_fromGivenRANGE( [ open_PRICE[i] - close_PRICE[i-1] for i in range( 1, len( close_PRICE ) ) ], nBINs_ = 31, aPrefixTEXT_ = "" )
0: ~ -432.00 LDF = 1 |____ 1.0 % _||____ 1 %
1: ~ -408.10 LDF = 1 |____ 1.0 % _||____ 2 %
2: ~ -384.19 LDF = 1 |____ 1.0 % _||____ 3 %
3: ~ -360.29 LDF = 0 |____ 0.0 % _||____ 3 %
4: ~ -336.39 LDF = 1 |____ 1.0 % _||____ 4 %
5: ~ -312.48 LDF = 1 |____ 1.0 % _||____ 5 %
6: ~ -288.58 LDF = 1 |____ 1.0 % _||____ 6 %
7: ~ -264.68 LDF = 0 |____ 0.0 % _||____ 6 %
8: ~ -240.77 LDF = 1 |____ 1.0 % _||____ 7 %
9: ~ -216.87 LDF = 3 |____ 3.0 % _||___ 10 %
10: ~ -192.97 LDF = 2 |____ 2.0 % _||___ 12 %
11: ~ -169.06 LDF = 1 |____ 1.0 % _||___ 13 %
12: ~ -145.16 LDF = 1 |____ 1.0 % _||___ 14 %
13: ~ -121.26 LDF = 2 |____ 2.0 % _||___ 16 %
14: ~ -97.35 LDF = 5 |____ 5.1 % _||___ 21 %
15: ~ -73.45 LDF = 3 |____ 3.0 % _||___ 24 %
16: ~ -49.55 LDF = 5 |____ 5.1 % _||___ 29 %
17: ~ -25.65 LDF = 18 |___ 18.2 % _||___ 47 %
18: ~ -1.74 LDF = 28 |___ 28.3 % _||___ 75 %
19: ~ 22.16 LDF = 5 |____ 5.1 % _||___ 80 %
20: ~ 46.06 LDF = 5 |____ 5.1 % _||___ 85 %
21: ~ 69.97 LDF = 2 |____ 2.0 % _||___ 87 %
22: ~ 93.87 LDF = 1 |____ 1.0 % _||___ 88 %
23: ~ 117.77 LDF = 4 |____ 4.0 % _||___ 92 %
24: ~ 141.68 LDF = 1 |____ 1.0 % _||___ 93 %
25: ~ 165.58 LDF = 1 |____ 1.0 % _||___ 94 %
26: ~ 189.48 LDF = 1 |____ 1.0 % _||___ 95 %
27: ~ 213.39 LDF = 1 |____ 1.0 % _||___ 96 %
28: ~ 237.29 LDF = 0 |____ 0.0 % _||___ 96 %
29: ~ 261.19 LDF = 1 |____ 1.0 % _||___ 97 %
30: ~ 285.10 LDF = 2 |____ 2.0 % _||__ 100 %
+0:00:06.234000
|
|
|>>> QuantFX.get_LDF_GDF_fromGivenRANGE( [ close_PRICE[i] - open_PRICE[i] for i in range( 1, len( close_PRICE ) ) ], nBINs_ = 31, aPrefixTEXT_ = "" )
0: ~ -523.00 LDF = 2 |____ 2.0 % _||____ 2 %
1: ~ -478.32 LDF = 1 |____ 1.0 % _||____ 3 %
2: ~ -433.65 LDF = 3 |____ 3.0 % _||____ 6 %
3: ~ -388.97 LDF = 1 |____ 1.0 % _||____ 7 %
4: ~ -344.29 LDF = 1 |____ 1.0 % _||____ 8 %
5: ~ -299.61 LDF = 2 |____ 2.0 % _||___ 10 %
6: ~ -254.94 LDF = 7 |____ 7.1 % _||___ 17 %
7: ~ -210.26 LDF = 3 |____ 3.0 % _||___ 20 %
8: ~ -165.58 LDF = 2 |____ 2.0 % _||___ 22 %
9: ~ -120.90 LDF = 5 |____ 5.1 % _||___ 27 %
10: ~ -76.23 LDF = 6 |____ 6.1 % _||___ 33 %
11: ~ -31.55 LDF = 22 |___ 22.2 % _||___ 55 %
12: ~ 13.13 LDF = 7 |____ 7.1 % _||___ 62 %
13: ~ 57.81 LDF = 5 |____ 5.1 % _||___ 67 %
14: ~ 102.48 LDF = 4 |____ 4.0 % _||___ 71 %
15: ~ 147.16 LDF = 8 |____ 8.1 % _||___ 79 %
16: ~ 191.84 LDF = 6 |____ 6.1 % _||___ 85 %
17: ~ 236.52 LDF = 2 |____ 2.0 % _||___ 87 %
18: ~ 281.19 LDF = 3 |____ 3.0 % _||___ 90 %
19: ~ 325.87 LDF = 2 |____ 2.0 % _||___ 92 %
20: ~ 370.55 LDF = 2 |____ 2.0 % _||___ 94 %
21: ~ 415.23 LDF = 3 |____ 3.0 % _||___ 97 %
22: ~ 459.90 LDF = 0 |____ 0.0 % _||___ 97 %
23: ~ 504.58 LDF = 0 |____ 0.0 % _||___ 97 %
24: ~ 549.26 LDF = 0 |____ 0.0 % _||___ 97 %
25: ~ 593.94 LDF = 1 |____ 1.0 % _||___ 98 %
26: ~ 638.61 LDF = 0 |____ 0.0 % _||___ 98 %
27: ~ 683.29 LDF = 0 |____ 0.0 % _||___ 98 %
28: ~ 727.97 LDF = 0 |____ 0.0 % _||___ 98 %
29: ~ 772.65 LDF = 0 |____ 0.0 % _||___ 98 %
30: ~ 817.32 LDF = 1 |____ 1.0 % _||__ 100 %
+0:01:13.172000