python - 用股票报价识别 Pandas 数据框中的价格波动/趋势

Question

我有一个带有 DatetimeIndex 和 ohlcv 股票报价列的 pandas Dataframe。我想提取满足特定阈值的价格波动/趋势:大于 0.3 美元的上升趋势/趋势和超过 -0.3 美元的下跌趋势/趋势。

df[:10]
                           close   high   low    open    volume
2014-05-09 09:30:00-04:00 187.5600 187.73 187.54 187.700 1922600
2014-05-09 09:31:00-04:00 187.4900 187.56 187.42 187.550 534400
2014-05-09 09:32:00-04:00 187.4200 187.51 187.35 187.490 224800
2014-05-09 09:33:00-04:00 187.5500 187.58 187.39 187.400 303700
2014-05-09 09:34:00-04:00 187.6700 187.67 187.53 187.560 438200
2014-05-09 09:35:00-04:00 187.6000 187.71 187.56 187.680 296400
2014-05-09 09:36:00-04:00 187.4100 187.67 187.38 187.600 329900
2014-05-09 09:37:00-04:00 187.3100 187.44 187.28 187.400 404000
2014-05-09 09:38:00-04:00 187.2600 187.37 187.26 187.300 912800
2014-05-09 09:39:00-04:00 187.2200 187.28 187.12 187.250 607700

在研究了 pandas 文档之后，Dataframe.apply() 似乎是一种方法，但我在构建函数时遇到了困难。由于我的编码能力总体上有限，所以我需要一些帮助。

global row_nr
row_nr = 1
def extract_swings()
    if row_nr == 1 : pivot = row.open ; row_nr += 1
    else : if (row.high-pivot) >= 0.3 : ????
    ... ????

df['swings'] = df.apply(extract_swings, axis=1)

结果应该是这样的:

df['swings'][:10]
2014-05-09 09:30:00-04:00 NaN
2014-05-09 09:31:00-04:00 NaN
2014-05-09 09:32:00-04:00 -0.35
2014-05-09 09:33:00-04:00 NaN
2014-05-09 09:34:00-04:00 NaN
2014-05-09 09:35:00-04:00 0.36
2014-05-09 09:36:00-04:00 NaN
2014-05-09 09:37:00-04:00 NaN
2014-05-09 09:38:00-04:00 NaN
2014-05-09 09:39:00-04:00 -0.59

更新:为了避免混淆，请求的函数应该如何通过数据框:

                           close    high   low    open    volume 
2014-05-09 09:30:00-04:00 187.5600 187.73 187.54 187.700 1922600
# this is the first line, first minute and we well take row.open 187.70 as \
# the starting point or first pivot
2014-05-09 09:31:00-04:00 187.4900 187.56 187.42 187.550 534400
# next minute we check if either (row.high - pivot) >= 0.3 or \
# (row.low-pivot) <= -0.3. Neither is true so nothing to do here.
2014-05-09 09:32:00-04:00 187.4200 187.51 187.35 187.490 224800
# next minute same check ... we see that row.low-pivot = -0.35. \
# We consider 187.35 a second pivot and the diff value -0.35 a first trend down
2014-05-09 09:33:00-04:00 187.5500 187.58 187.39 187.400 303700
# next minute we check if the identified trend/swing down goes further \
# down by having a row.low lower than previous row.low. If we would \
# have found here a new lower row.low that would be the second pivot \
# and we would forget about 187.35 as being a pivot ... and so on. \
# We don't see that on this row, instead we see prices are higher than \
# previous row, so we start checking the diff for a potential up trend \
# starting from second pivot 187.35. As long as we do not encounter a \
# higher high with over 0.3 above last pivot we are still within the identified down trend. 
2014-05-09 09:34:00-04:00 187.6700 187.67 187.53 187.560 438200
# we don't see a lower low to reconsider the second pivot neither \
# a (row.high- second_pivot) >= 0.3
2014-05-09 09:35:00-04:00 187.6000 187.71 187.56 187.680 296400
# here we see (row.high- second_pivot) = 0.36. We consider 187.71 as \
# a third_pivot and the diff value 0.36 as an up trend (from second pivot to here)
2014-05-09 09:36:00-04:00 187.4100 187.67 187.38 187.600 329900
# next minute we check if the identified trend/swing up goes further up \
# by having a row.high higher than third pivot. If we would have found here \
# a new higher row.high that would be the third pivot and we would forget \
# about 187.71 as being a pivot ... and so on. We don't see that on this row,\
# instead we see prices are lower than previous row, so we start \
# checking the diff for a potential down trend starting from third \
# pivot 187.71. As long as we do not encounter a lower low with \
# over 0.3 below last pivot we are still within the identified up trend.
2014-05-09 09:37:00-04:00 187.3100 187.44 187.28 187.400 404000
# we find here a (row.low - third_pivot) = 0.43 so we have identified \
# a new down trend starting from third pivot and now we have a potential\
# fourth pivot 187.28 
2014-05-09 09:38:00-04:00 187.2600 187.37 187.26 187.300 912800
# we find here a lower low so we don't consider 187.28 the fourth \
# pivot anymore but this lower low 187.26
2014-05-09 09:39:00-04:00 187.2200 187.28 187.12 187.250 607700
# we find here a lower low so we don't consider 187.26 the fourth pivot anymore \
# but this lower low 187.12. Being this the lowest low we consider this one \
# to be the fourth pivot and the diff 187.12-187.71=-0.59 as a downtrend with that value 

Answer 1

这有点棘手，因为在找到下一个潜在支点之前，您不能将一个点标记为支点(即，如果您处于上升趋势中，则在找到足够低的低点之前不能说它已经完成)。

这段代码可以解决问题 - 为方便起见，我已将您的数据放在 tmpData.txt 文件中，并获得了所需的结果。请检查

def get_pivots():
    data = pd.DataFrame.from_csv('tmpData.txt')
    data['swings'] = np.nan

    pivot = data.irow(0).open
    last_pivot_id = 0
    up_down = 0

    diff = .3

    for i in range(0, len(data)):
        row = data.irow(i)

        # We don't have a trend yet
        if up_down == 0:
            if row.low < pivot - diff:
                data.ix[i, 'swings'] = row.low - pivot
                pivot, last_pivot_id = row.low, i
                up_down = -1
            elif row.high > pivot + diff:
                data.ix[i, 'swings'] = row.high - pivot
                pivot, last_pivot_id = row.high, i
                up_down = 1

        # Current trend is up
        elif up_down == 1:
            # If got higher than last pivot, update the swing
            if row.high > pivot:
                # Remove the last pivot, as it wasn't a real one
                data.ix[i, 'swings'] = data.ix[last_pivot_id, 'swings'] + (row.high - data.ix[last_pivot_id, 'high'])
                data.ix[last_pivot_id, 'swings'] = np.nan
                pivot, last_pivot_id = row.high, i
            elif row.low < pivot - diff:
                data.ix[i, 'swings'] = row.low - pivot
                pivot, last_pivot_id = row.low, i
                # Change the trend indicator
                up_down = -1

        # Current trend is down
        elif up_down == -1:
             # If got lower than last pivot, update the swing
            if row.low < pivot:
                # Remove the last pivot, as it wasn't a real one
                data.ix[i, 'swings'] = data.ix[last_pivot_id, 'swings'] + (row.low - data.ix[last_pivot_id, 'low'])
                data.ix[last_pivot_id, 'swings'] = np.nan
                pivot, last_pivot_id = row.low, i
            elif row.high > pivot - diff:
                data.ix[i, 'swings'] = row.high - pivot
                pivot, last_pivot_id = row.high, i
                # Change the trend indicator
                up_down = 1

    print data

输出:

date                  close  high    low     open    volume    swings                                            
2014-05-09 13:30:00  187.56  187.73  187.54  187.70  1922600     NaN
2014-05-09 13:31:00  187.49  187.56  187.42  187.55   534400     NaN
2014-05-09 13:32:00  187.42  187.51  187.35  187.49   224800   -0.35
2014-05-09 13:33:00  187.55  187.58  187.39  187.40   303700     NaN
2014-05-09 13:34:00  187.67  187.67  187.53  187.56   438200     NaN
2014-05-09 13:35:00  187.60  187.71  187.56  187.68   296400    0.36
2014-05-09 13:36:00  187.41  187.67  187.38  187.60   329900     NaN
2014-05-09 13:37:00  187.31  187.44  187.28  187.40   404000     NaN
2014-05-09 13:38:00  187.26  187.37  187.26  187.30   912800     NaN
2014-05-09 13:39:00  187.22  187.28  187.12  187.25   607700   -0.59