[英]Rolling Window of Local Minima/Maxima
I've made a script (shown below) that helps determine local maxima points using historical stock data.我制作了一个脚本(如下所示),可帮助使用历史股票数据确定局部最大值点。 It uses the daily highs to mark out local resistance levels.
它使用每日高点来标记当地的阻力水平。 Works great, but what I would like is, for any given point in time (or row in the stock data), I want to know what the most recent resistance level was just prior to that point.
效果很好,但我想要的是,对于任何给定的时间点(或股票数据中的行),我想知道在该点之前的最近阻力位是多少。 I want this in it's own column in the dataset.
我希望它在数据集中它自己的列中。 So for instance:
所以例如:
The top grey line is the highs for each day, and the bottom grey line was the close of each day.顶部的灰线是每天的高点,底部的灰线是每天的收盘价。 So roughly speaking, the dataset for that section would look like this:
粗略地说,该部分的数据集如下所示:
High Close
216.8099976 216.3399963
215.1499939 213.2299957
214.6999969 213.1499939
215.7299957 215.2799988 <- First blue dot at high
213.6900024 213.3699951
214.8800049 213.4100037 <- 2nd blue dot at high
214.5899963 213.4199982
216.0299988 215.8200073
217.5299988 217.1799927 <- 3rd blue dot at high
216.8800049 215.9900055
215.2299957 214.2400055
215.6799927 215.5700073
....
Right now, this script looks at the entire dataset at once to determine the local maxima indexes for the highs, and then for any given point in the stock history (ie any given row), it looks for the NEXT maxima in the list of all maximas found.现在,这个脚本一次查看整个数据集以确定高点的局部最大值索引,然后对于股票历史中的任何给定点(即任何给定行),它在所有列表中查找 NEXT 最大值发现最大值。 This would be a way to determine where the next resistance level is, but I don't want that due to a look ahead bias.
这将是一种确定下一个阻力位在哪里的方法,但由于前瞻性偏见,我不希望这样做。 I just want to have a column of the most recent past resistance level or maybe even the latest 2 recent points in 2 columns.
我只想有一个最近的过去阻力位的列,或者甚至是 2 列中最近的 2 个最近点。 That would be ideal actually.
这实际上是理想的。
So my final output would look like this for the 1 column:因此,对于 1 列,我的最终输出将如下所示:
High Close Most_Rec_Max
216.8099976 216.3399963 0
215.1499939 213.2299957 0
214.6999969 213.1499939 0
215.7299957 215.2799988 0
213.6900024 213.3699951 215.7299957
214.8800049 213.4100037 215.7299957
214.5899963 213.4199982 214.8800049
216.0299988 215.8200073 214.8800049
217.5299988 217.1799927 214.8800049
216.8800049 215.9900055 217.5299988
215.2299957 214.2400055 217.5299988
215.6799927 215.5700073 217.5299988
....
You'll notice that the dot only shows up in most recent column after it has already been discovered.您会注意到该点仅在被发现后才显示在最近的列中。
Here is the code I am using:这是我正在使用的代码:
real_close_prices = df['Close'].to_numpy()
highs = df['High'].to_numpy()
max_indexes = (np.diff(np.sign(np.diff(highs))) < 0).nonzero()[0] + 1 # local max
# +1 due to the fact that diff reduces the original index number
max_values_at_indexes = highs[max_indexes]
curr_high = [c for c in highs]
max_values_at_indexes.sort()
for m in max_values_at_indexes:
for i, c in enumerate(highs):
if m > c and curr_high[i] == c:
curr_high[i] = m
#print(nextbig)
df['High_Resistance'] = curr_high
# plot
plt.figure(figsize=(12, 5))
plt.plot(x, highs, color='grey')
plt.plot(x, real_close_prices, color='grey')
plt.plot(x[max_indexes], highs[max_indexes], "o", label="max", color='b')
plt.show()
Hoping someone will be able to help me out with this.希望有人能够帮助我解决这个问题。 Thanks!
谢谢!
Here is one approach.这是一种方法。 Once you know where the peaks are, you can store peak indices in
p_ids
and peak values in p_vals
.一旦知道峰值在哪里,您就可以将峰值索引存储在
p_ids
,将峰值存储在p_vals
。 To assign the k
'th most recent peak, note that p_vals[:-k]
will occur at p_ids[k:]
.要分配第
k
个最近的峰值,请注意p_vals[:-k]
将出现在p_ids[k:]
。 The rest is forward filling.其余的是向前填充。
# find all local maxima in the series by comparing to shifted values
peaks = (df.High > df.High.shift(1)) & (df.High > df.High.shift(-1))
# pass peak value if peak is achieved and NaN otherwise
# forward fill with previous peak value & handle leading NaNs with fillna
df['Most_Rec_Max'] = (df.High * peaks.replace(False, np.nan)).ffill().fillna(0)
# for finding n-most recent peak
p_ids, = np.where(peaks)
p_vals = df.High[p_ids].values
for n in [1,2]:
col_name = f'{n+1}_Most_Rec_Max'
df[col_name] = np.nan
df.loc[p_ids[n:], col_name] = p_vals[:-n]
df[col_name].ffill(inplace=True)
df[col_name].fillna(0, inplace=True)
# High Close Most_Rec_Max 2_Most_Rec_Max 3_Most_Rec_Max
# 0 216.809998 216.339996 0.000000 0.000000 0.000000
# 1 215.149994 213.229996 0.000000 0.000000 0.000000
# 2 214.699997 213.149994 0.000000 0.000000 0.000000
# 3 215.729996 215.279999 215.729996 0.000000 0.000000
# 4 213.690002 213.369995 215.729996 0.000000 0.000000
# 5 214.880005 213.410004 214.880005 215.729996 0.000000
# 6 214.589996 213.419998 214.880005 215.729996 0.000000
# 7 216.029999 215.820007 214.880005 215.729996 0.000000
# 8 217.529999 217.179993 217.529999 214.880005 215.729996
# 9 216.880005 215.990006 217.529999 214.880005 215.729996
# 10 215.229996 214.240006 217.529999 214.880005 215.729996
# 11 215.679993 215.570007 217.529999 214.880005 215.729996
I just came across this function that might help you a lot: scipy.signal.find_peaks .我刚刚遇到了这个可能对你有很大帮助的函数: scipy.signal.find_peaks 。
Based on your sample dataframe, we can do the following:根据您的示例数据框,我们可以执行以下操作:
from scipy.signal import find_peaks
## Grab the minimum high value as a threshold.
min_high = df["High"].min()
### Run the High values through the function. The docs explain more,
### but we can set our height to the minimum high value.
### We just need one out of two return values.
peaks, _ = find_peaks(df["High"], height=min_high)
### Do some maintenance and add a column to mark peaks
# Merge on our index values
df1 = df.merge(peaks_df, how="left", left_index=True, right_index=True)
# Set non-null values to 1 and null values to 0; Convert column to integer type.
df1.loc[~df1["local_high"].isna(), "local_high"] = 1
df1.loc[df1["local_high"].isna(), "local_high"] = 0
df1["local_high"] = df1["local_high"].astype(int)
Then, your dataframe should look like the following:然后,您的数据框应如下所示:
High Low local_high
0 216.809998 216.339996 0
1 215.149994 213.229996 0
2 214.699997 213.149994 0
3 215.729996 215.279999 1
4 213.690002 213.369995 0
5 214.880005 213.410004 1
6 214.589996 213.419998 0
7 216.029999 215.820007 0
8 217.529999 217.179993 1
9 216.880005 215.990005 0
10 215.229996 214.240005 0
11 215.679993 215.570007 0
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