[SettingWithCopyWarning] for getting ATR

[yurenji]

my code:

    @classmethod
    def get_atr(cls, candles):
        stock = StockDataFrame.retype(TCandle.to_df(candles))
        return list(stock.get('atr'))

I got the following warning:

  /Users/yurenji/.conda/envs/tangle/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: 
  A value is trying to be set on a copy of a slice from a DataFrame
  
  See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
    self._setitem_with_indexer(indexer, value)

-- Docs: http://doc.pytest.org/en/latest/warnings.html

I tried 'macdh' and 'sma', they don't have this issue.

[erikgqp8645]

Hello, your problem has been solved.
I've had the same problem as you, and I think I've probably found it

[jealous]

Hi @erikgqp8645 , could you share the solution?

[erikgqp8645]

你好！
@jealous
我看到你是上海的，并且看到里面有人讲中文，就冒昧的讲中文试下，因为我的英文实在很差劲。
我想是我高兴的太早了，我并没有找到问题发生的根本原因，或许是距离真相更近一步了。
我觉得问题可能发生在数据上，我把我的数据和结果都黏贴上来，希望我们一起解决他。

原始代码是这样，请原谅我不会插入代码
import math
import pandas as pd
import numpy as np
import tushare as ts
import datetime
import matplotlib.pyplot as plt
import stockstats

设置做大显示列和行，中间不显示省略号

pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_colwidth', 1000)
pd.set_option('display.width', 1000) # 设置多列不换行

读取兔子上的数据

begin_time = '2017-02-01'
end_time = '2017-11-01'
code = "000001"
stock = ts.get_hist_data(code, start=begin_time, end=end_time)
stock["date"] = stock.index.values # 增加日期列。
stock = stock.sort_index(0) # 将数据按照日期排序下。

初始化统计类

stockStat = stockstats.StockDataFrame.retype(stock)
stockStat._get_atr(stockStat, window=20)
print(stock)

读取df的数据存在问题

df = pd.read_csv("E:\Text_Guo\Data\btc\BITFINEX_BTCUSD_20180101_1T.csv", skiprows=1)

stock["date"] = stock.index.values # 增加日期列。

df = stockstats.sort_index(0) # 将数据按照日期排序下。

初始化统计类

stockStat_df = stockstats.StockDataFrame.retype(df)
stockStat_df._get_atr(stockStat_df, window=20)
print(stockStat_df)

第一个结果是这样
open high close low volume price_change p_change ma5 ma10 ma20 v_ma5 v_ma10 v_ma20 close_-1_s tr atr_20
date
2017-02-02 9.45 10.26 10.26 9.33 19345.00 0.93 9.97 9.468 9.320 9.234 327000.59 426903.19 397560.55 NaN NaN NaN
2017-02-03 9.34 9.36 9.26 9.23 315472.25 -0.07 -0.75 9.476 9.332 9.242 306035.17 390133.84 398203.79 10.26 1.03 1.030000
2017-02-06 9.26 9.32 9.31 9.26 516786.12 0.05 0.54 9.484 9.348 9.249 315343.58 387257.21 401051.07 9.26 0.06 0.532564
2017-02-07 9.31 9.32 9.30 9.27 396884.97 -0.01 -0.11 9.492 9.361 9.256 333840.18 369518.77 398428.85 9.31 0.05 0.363392
2017-02-08 9.29 9.30 9.30 9.24 360272.41 0.00 0.00 9.486 9.373 9.263 321752.15 361774.72 399223.82 9.30 0.06 0.281612
2017-02-09 9.30 9.33 9.31 9.28 342855.12 0.01 0.11 9.296 9.382 9.272 386454.17 356727.38 398458.87 9.30 0.05 0.230420
2017-02-10 9.32 9.36 9.33 9.31 482743.25 0.02 0.21 9.310 9.393 9.281 419908.37 362971.77 404541.95 9.31 0.05 0.196367
2017-02-13 9.34 9.44 9.41 9.33 638364.75 0.08 0.86 9.330 9.407 9.294 444224.10 379783.84 424407.49 9.33 0.11 0.182052
2017-02-14 9.41 9.42 9.40 9.37 362404.59 -0.01 -0.11 9.350 9.421 9.307 437328.02 385584.10 427356.18 9.41 0.05 0.162435
2017-02-15 9.40 9.54 9.45 9.39 756613.06 0.05 0.53 9.380 9.433 9.322 516596.15 419174.15 443786.49 9.40 0.15 0.160753
2017-02-16 9.45 9.50 9.46 9.42 411161.12 0.01 0.11 9.410 9.353 9.337 530257.35 458355.76 442629.48 9.45 0.08 0.150691
2017-02-17 9.46 9.49 9.39 9.37 423774.97 -0.07 -0.74 9.422 9.366 9.349 518463.70 469186.04 429659.94 9.46 0.12 0.147132
2017-02-20 9.40 9.58 9.56 9.40 898755.38 0.17 1.81 9.452 9.391 9.370 570541.82 507382.96 447320.09 9.39 0.19 0.151795
2017-02-21 9.55 9.62 9.57 9.54 646584.19 0.01 0.10 9.486 9.418 9.390 627377.74 532352.88 450935.83 9.56 0.08 0.144419
2017-02-22 9.57 9.57 9.57 9.50 462966.12 0.00 0.00 9.510 9.445 9.409 568648.36 542622.26 452198.49 9.57 0.07 0.137156
2017-02-23 9.55 9.57 9.51 9.48 335327.81 -0.06 -0.63 9.520 9.465 9.424 553481.69 541869.52 449298.45 9.57 0.09 0.132763
2017-02-24 9.50 9.54 9.50 9.48 332500.12 -0.01 -0.10 9.542 9.482 9.438 535226.72 526845.21 444908.49 9.51 0.06 0.126265
2017-02-27 9.50 9.50 9.43 9.42 407341.12 -0.07 -0.74 9.516 9.484 9.446 436943.87 503742.85 441763.34 9.50 0.08 0.122289
2017-02-28 9.43 9.51 9.48 9.42 369719.69 0.05 0.53 9.498 9.492 9.457 381570.97 504474.36 445029.23 9.43 0.09 0.119611
2017-03-01 9.49 9.55 9.49 9.47 346993.72 0.01 0.10 9.482 9.496 9.465 358376.49 463512.42 441343.29 9.48 0.08 0.116430
2017-03-02 9.51 9.54 9.43 9.42 403628.72 -0.06 -0.63 9.466 9.493 9.423 372036.67 462759.18 460557.47 9.49 0.12 0.116708
2017-03-03 9.41 9.43 9.40 9.36 342655.09 -0.03 -0.32 9.446 9.494 9.430 374067.67 454647.20 461916.62 9.43 0.07 0.113167
2017-03-06 9.40 9.46 9.45 9.39 404511.38 0.05 0.53 9.450 9.483 9.437 373501.72 405222.80 456302.88 9.40 0.07 0.109976
2017-03-07 9.44 9.46 9.45 9.40 294672.81 0.00 0.00 9.444 9.471 9.445 358492.34 370031.66 451192.27 9.45 0.06 0.106369
2017-03-08 9.43 9.45 9.42 9.40 244438.41 -0.03 -0.32 9.430 9.456 9.451 337981.28 348178.89 445400.57 9.45 0.05 0.102388
2017-03-09 9.41 9.43 9.38 9.36 378169.91 -0.04 -0.42 9.420 9.443 9.454 332889.52 352463.10 447166.31 9.42 0.07 0.100147
2017-03-10 9.38 9.41 9.40 9.36 390182.25 0.02 0.21 9.420 9.433 9.458 342394.95 358231.31 442538.26 9.38 0.05 0.096742
2017-03-13 9.39 9.45 9.44 9.37 545304.44 0.04 0.43 9.418 9.434 9.459 370553.56 372027.64 437885.25 9.40 0.08 0.095626
2017-03-14 9.43 9.46 9.44 9.41 404484.38 0.00 0.00 9.416 9.430 9.461 392515.88 375504.11 439989.23 9.44 0.05 0.092633
2017-03-15 9.42 9.48 9.48 9.42 546560.88 0.04 0.42 9.428 9.429 9.463 452940.37 395460.83 429486.63 9.44 0.06 0.090525
2017-03-16 9.48 9.53 9.52 9.45 635953.81 0.04 0.42 9.456 9.438 9.466 504497.15 418693.34 440726.26 9.48 0.08 0.089855
2017-03-17 9.45 9.46 9.31 9.30 1583647.00 -0.21 -2.21 9.438 9.429 9.462 743190.10 542792.53 498719.86 9.52 0.22 0.098029
2017-03-20 9.29 9.31 9.25 9.22 715021.75 -0.06 -0.64 9.400 9.409 9.446 777133.56 573843.56 489533.18 9.31 0.09 0.097531
2017-03-21 9.25 9.26 9.24 9.20 554648.75 -0.01 -0.11 9.360 9.388 9.430 807166.44 599841.16 484936.41 9.25 0.06 0.095231
2017-03-22 9.20 9.22 9.16 9.14 566123.62 -0.08 -0.87 9.296 9.362 9.409 811078.99 632009.68 490094.28 9.24 0.10 0.095520

第二个结果是这样
D:\anaconda3\lib\site-packages\pandas\core\indexing.py:189: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self.setitem_with_indexer(indexer, value)
candle_begin_time open high low close volume close-1_s tr atr_20
0 2018-01-01 00:00:00 13763.00000 13788.000000 13752.182339 13753.00000 5.133268 NaN NaN NaN
1 2018-01-01 00:01:00 13763.00000 13773.000000 13741.000000 13772.00000 15.536082 13753.00000 32.000000 32.000000
2 2018-01-01 00:02:00 13774.00000 13777.000000 13741.000000 13742.00000 16.958926 13772.00000 36.000000 34.051282
3 2018-01-01 00:03:00 13741.00000 13742.000000 13721.000000 13722.00000 25.783042 13742.00000 21.000000 29.475898
4 2018-01-01 00:04:00 13722.00000 13722.000000 13709.000000 13709.00000 11.431000 13722.00000 13.000000 25.034806
5 2018-01-01 00:05:00 13710.00000 13710.000000 13559.000000 13628.00000 169.517133 13709.00000 151.000000 52.876225
6 2018-01-01 00:06:00 13628.00000 13637.000000 13621.000000 13625.00000 21.351554 13628.00000 16.000000 45.916033
7 2018-01-01 00:07:00 13624.00000 13624.000000 13591.000000 13607.00000 23.800193 13625.00000 34.000000 43.940974
8 2018-01-01 00:08:00 13602.00000 13647.000000 13602.000000 13639.00000 26.622142 13607.00000 45.000000 44.098296
9 2018-01-01 00:09:00 13639.00000 13676.716245 13607.000000 13640.00000 45.154382 13639.00000 69.716245 47.562516
10 2018-01-01 00:10:00 13639.00000 13641.000000 13600.000000 13640.00000 36.542425 13640.00000 41.000000 46.744784
11 2018-01-01 00:11:00 13640.00000 13650.000000 13624.000000 13624.00000 26.901903 13640.00000 26.000000 44.339312
12 2018-01-01 00:12:00 13624.00000 13637.143107 13611.960201 13615.00000 8.482073 13624.00000 25.182906 42.255463

[erikgqp8645]

第一个数据是没有问题的，第二个数据存在问题。

[jealous]

我也不清楚是哪个操作触发了这个警告。你可以忽略这个警告，目前看起来不会对数据有影响。

[erikgqp8645]

好的，我试着忽略他。
谢谢。

[jealous]

Fixed in 0.3.2.