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python - 如何计算和绘制时间序列的多个线性趋势?

转载 作者:行者123 更新时间:2023-11-30 22:43:02 35 4
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将线性趋势拟合到一组数据非常简单。但如何才能将多条趋势线拟合到一个时间序列中呢?我将上涨和下跌趋势定义为价格高于或低于指数移动平均线。当价格高于 EMA 时,我需要拟合正趋势,当趋势转为负时,我需要拟合新的负趋势线,依此类推。在我的代码中,pandas 数据框中的 market_data['Signal'] 告诉我趋势是向上 +1 还是向下 -1。

我猜我需要某种循环,但我无法弄清楚逻辑......

import pandas as pd
import pandas_datareader.data as web
import datetime as dt
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.dates as mdates

#Colecting data
market = '^DJI'
end = dt.datetime(2016, 12, 31)
start = dt.date(end.year-10, end.month, end.day)
market_data = web.DataReader(market, 'yahoo', start, end)

#Calculating EMA and difference
market_data['ema'] = market_data['Close'].ewm(200).mean()
market_data['diff_pc'] = (market_data['Close'] / market_data['ema']) - 1

#Defining bull/bear signal
TH = 0
market_data['Signal'] = np.where(market_data['diff_pc'] > TH, 1, 0)
market_data['Signal'] = np.where(market_data['diff_pc'] < -TH, -1, market_data['Signal'])

为了拟合趋势线,我想使用 numpy polyfit

x = np.array(mdates.date2num(market_data.index.to_pydatetime()))
fit = np.polyfit(x, market_data['Close'], 1)

理想情况下,我只想绘制信号持续超过 n 个周期的趋势。

结果应该是这样的:

enter image description here

最佳答案

这里有一个解决方案。 min_signal 是改变趋势所需的连续信号的数量。我导入了Seaborn以获得更好看的情节,但没有那行它仍然可以工作:

import pandas as pd
import pandas_datareader.data as web
import datetime as dt
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.dates as mdates

#Colecting data
market = '^DJI'
end = dt.datetime(2016, 12, 31)
start = dt.date(end.year-10, end.month, end.day)
market_data = web.DataReader(market, 'yahoo', start, end)

#Calculating EMA and difference
market_data['ema'] = market_data['Close'].ewm(200).mean()
market_data['diff_pc'] = (market_data['Close'] / market_data['ema']) - 1

#Defining bull/bear signal
TH = 0
market_data['Signal'] = np.where(market_data['diff_pc'] > TH, 1, 0)
market_data['Signal'] = np.where(market_data['diff_pc'] < -TH, -1, market_data['Signal'])


# Plot data and fits

import seaborn as sns # This is just to get nicer plots

signal = market_data['Signal']

# How many consecutive signals are needed to change trend
min_signal = 2

# Find segments bounds
bounds = (np.diff(signal) != 0) & (signal[1:] != 0)
bounds = np.concatenate(([signal[0] != 0], bounds))
bounds_idx = np.where(bounds)[0]
# Keep only significant bounds
relevant_bounds_idx = np.array([idx for idx in bounds_idx if np.all(signal[idx] == signal[idx:idx + min_signal])])
# Make sure start and end are included
if relevant_bounds_idx[0] != 0:
relevant_bounds_idx = np.concatenate(([0], relevant_bounds_idx))
if relevant_bounds_idx[-1] != len(signal) - 1:
relevant_bounds_idx = np.concatenate((relevant_bounds_idx, [len(signal) - 1]))

# Iterate segments
for start_idx, end_idx in zip(relevant_bounds_idx[:-1], relevant_bounds_idx[1:]):
# Slice segment
segment = market_data.iloc[start_idx:end_idx + 1, :]
x = np.array(mdates.date2num(segment.index.to_pydatetime()))
# Plot data
data_color = 'green' if signal[start_idx] > 0 else 'red'
plt.plot(segment.index, segment['Close'], color=data_color)
# Plot fit
coef, intercept = np.polyfit(x, segment['Close'], 1)
fit_val = coef * x + intercept
fit_color = 'yellow' if coef > 0 else 'blue'
plt.plot(segment.index, fit_val, color=fit_color)

这是结果:

Result

关于python - 如何计算和绘制时间序列的多个线性趋势?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/41906679/

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