How to calculate and plot multiple linear trends for a time series?

Here is a solution. min_signal is the number of consecutive signals in a row that are needed to change trend. I imported Seaborn to get a better-looking plot, but it works all the same without that line:

import pandas as pdimport pandas_datareader.data as webimport datetime as dtimport numpy as npimport matplotlib.pyplot as pltimport matplotlib.patches as mpatchesimport matplotlib.dates as mdates#Colecting datamarket = '^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 differencemarket_data['ema'] = market_data['Close'].ewm(200).mean()market_data['diff_pc'] = (market_data['Close'] / market_data['ema']) - 1#Defining bull/bear signalTH = 0market_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 fitsimport seaborn as sns  # This is just to get nicer plotssignal = market_data['Signal']# How many consecutive signals are needed to change trendmin_signal = 2# Find segments boundsbounds = (np.diff(signal) != 0) & (signal[1:] != 0)bounds = np.concatenate(([signal[0] != 0], bounds))bounds_idx = np.where(bounds)[0]# Keep only significant boundsrelevant_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 includedif 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 segmentsfor 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)

This is the result: