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Everyone Can Visualize Stock Trends With These Python Codes!
Ayrat Murtazin
March 07, 2025
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This is how we can enhance stock picking with Python
chart generated by aurthor with yahoo finance data
I received very good feedback on my recent post using Logarithmic Regression to spot stock trends.
Let’s see how we can use Python to help removing the noise from the market as there are different “top picks” everyday, in order to not fall into a “pure hype with no potential gain” pitfall, while we are reading what is trending, we should be able to also identfy which trend can sustain, which is near the peak, and which has high potential to become the next rising star.
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Reading the Yahoo Screener page
I like to quickly glance in the morning the Yahoo Finance’s Top most active stock list. What I was trying to look for are:
what kind of sector the market (technology, enegery, retail, etc) is trending today
what kind of asset (Crypto, Currency, Stock, ETF, etc) people are searching most today
what stock / ticker has news that I might have missed hence the stock appears in this list today
I highlighted today in every sentence as it is really a short term market sentiment that people are chasing, today. It may or may not have a longer term future. Maybe these are just noise.
This is what you will get when you are reading the yahoo (US) Most Active screener above:
You need to click into each stock and read the chart before you can get a feeling the trend of the stock in a longer time frame.
I am lazy to do it one by one, so I write a chartting python program just to help me visulize “where we are” with the stocks under this screener.
The stock screener
First as usual, let’s import all necessary modules, I think I don’t need to further explain as these are the usual suspects :
import pandas as pd # Dataframe
from datetime import datetime # Date conversion
import math # some algo required
import requests # to help scrapping yahoo webpage
import numpy as np # some calculation and nan value checking
from bs4 import BeautifulSoup # for scrapping and interpret web scrap result
import matplotlib.pyplot as plt # plotting of charts
import matplotlib.dates as mdates
from matplotlib.dates import DateFormatter
import yfinance as yf # download yahoo finance dataScrapping the Yahoo page
I want the program to scrap the yahoo screener page so I use this routine which I have introduced in my another article about intrinsic value calculation:
def read_html_table(source):
header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36 '}
res = requests.get(source,headers=header, timeout=20)
if res.status_code != 200:
res = requests.get(source,headers=header, timeout=20) #try 1 more time
return None, res.status_code, res.text
# soup = BeautifulSoup(res.content, "lxml")
soup = BeautifulSoup(res.content, "html.parser")
if 'Select All' in res.text:
for tag in soup.find_all("span", {'class':'Fz(0)'}): #remove those select checkboxes if any
tag.replaceWith('')
table = soup.find_all('table')
if len(table)==0:
print ('something very wrong!')
return None
# return symbol list
df = pd.read_html(str(table))[0]
df['Symbol']=df['Symbol'].str.split().str[0] # adjust according to Yahoo table format and extract the symbols ONLY not with company name
return dfWe then need a main routine to read the symbol of Most Active stocks from the above page, and download one by one each stock’s historical data and plot it into chart.
I use Matplotlib module to plot the chart and with some matrix arrangement, I try to plot the subplots in a NxN grid as below.
The input of this routine is
link : the Yahoo screener link just like above. You can provide any Yahoo screener link as the layout of the webpage is the same.
startdate: I would like to further look back into months, years, decade of how this stock perform, so I use the argument to give me flexibility when retreiving stock historical data
For easy visulization, I used red and green colour to indicate as chart background of each stock whether it has up or down from yesterday.
You can simply tweet it and modify the conditional statement
“if (pct_change>=0):”
to some other algorithm that you are working on, so that the colour can indicate a BUY or SELL signal. We should always make our lives easier, no?
def stock_screener(link,startdate):
symbol_list =read_html_table(link)
if symbol_list.empty:
raise RuntimeError('yahoo trending-tickers error')
figrow=math.ceil(math.sqrt(len(symbol_list)))
figcol=math.ceil(math.sqrt(len(symbol_list)))
if figrow*figcol-len(symbol_list) >= figrow: # find the best fit square array of charts
figrow-=1
dynamic_dpi = min(figrow * figcol * 10, 1200)
dynamic_fontsize = max(8-figcol,4) #max(100/(figcol*figrow),5)
# prepare the chart
fig, axes = plt.subplots(figrow,figcol, figsize=(figcol, figrow), dpi=600, squeeze=False, sharey=False,sharex='col')
# Read finance data from Yahoo
all_stock_data=yf.download(symbol_list.Symbol.to_list(),start=startdate,interval='1d')
set(all_stock_data.columns.get_level_values(0))
all_stock_data=all_stock_data.reset_index()
for i,s in enumerate(symbol_list.Symbol): # iterate for every stock indices
tickerDf=pd.DataFrame()
ax = axes[int(i%figrow),int(i/figrow)]
tickerDf['Date']=all_stock_data['Date']
tickerDf['Close']=all_stock_data['Close'][s]
""" """
""" +------- SUBPLOT each symbol chart -----+ """
""" """
company_name_len=16
title_name=(symbol_list[symbol_list['Symbol']==s].iloc[0])['Name']
title_name=title_name[:company_name_len]
titlecolor='black'
facecolor='white'
# Plot stock chart
ax.plot(tickerDf['Date'],tickerDf['Close'],color='black',linewidth=0.6, alpha=1)
pct_change=0
# Here we try to use background color to indicate stock changes
if len(tickerDf)>3 : # try to avoid new stock with less than 3 days of data
if math.isnan(tickerDf['Close'].iloc[-1]): #sometimes yahoo returns current date data as NaN as the market hasn't open
current_price=tickerDf['Close'].iloc[-2] #tickerinfoData.get('ask')
previous_price=tickerDf['Close'].iloc[-3]
else:
current_price=tickerDf['Close'].iloc[-1] #tickerinfoData.get('ask')
previous_price=tickerDf['Close'].iloc[-2]
pct_change=((current_price-previous_price)/previous_price)*100
# Colour condition #
if (pct_change>=0): # change background colour depends on % change
todaytrendsymbol='⇧'
titlecolor='darkgreen'
facecolor='palegreen'
else:
todaytrendsymbol='⇩'
titlecolor='red'
facecolor='mistyrose'
# use facecolor to indicate Up/Down for easy visualization
ax.patch.set_facecolor(facecolor)
# let's beautify the chart a bit
ax.grid(True, color='silver',linewidth=0.5)
ax.tick_params(axis='x',labelrotation=90)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m'))
ax.xaxis.set_tick_params(labelsize=dynamic_fontsize)
ax.yaxis.set_tick_params(labelsize=dynamic_fontsize)
# Finally, add a title to the figure
title=title_name+'\n('+s+')'+\
str('%.2f'%current_price)+\
todaytrendsymbol+str('%.2f'%pct_change)+'%'
ax.set_title(title, fontweight='bold',color=titlecolor,fontsize=dynamic_fontsize)
if (i==0): # at each bottom row set the xaxis as date tick
for j in range(len(symbol_list),int(figrow*figcol)):
ax = axes[ int(j%figrow),int(j/figrow)]
ax.tick_params(axis='x',labelrotation=90)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m'))
ax.xaxis.set_tick_params(labelsize=dynamic_fontsize)
ax.yaxis.set_tick_params(labelsize=dynamic_fontsize)
plt.subplots_adjust(wspace=0.12, hspace=0.1)
today=datetime.now().strftime("%Y-%m-%d")
fig.suptitle(link+'\n'+startdate+'~'+today, fontweight ="bold",y=1, fontsize=dynamic_fontsize)
fig.tight_layout()
fig.savefig('/Users/user/Downloads/stock_screener.jpg',dpi=400,bbox_inches='tight')
return TrueIf now we call this routine with below, you will have the following chart.
if __name__ == '__main__':
url='https://finance.yahoo.com/trending-tickers'
stock_screener(url,startdate='2023-01-01')Viola!!!! Isn’t this beautiful and easy? Below are the result of 02–02–2024 pre-market.
What are we reading?
We see from above charts, many stocks are on the rise from yesterday. META has after-market rise even over 15% as of 02–02–2024!
The market is still very hot and very technology focus, maybe “too focus” in technology mainly.
Shall I follow the crowd? Is there still room before the Big Crash that everyone has been talking about? Looking at the above charts we can deduce the market sentiment is still on positive side. The problem is always about timing.
Applying analysis to the chart
We can simply change or add to the above chart our indicators to help us see through the price alone.
If I simply change the line :
# Plot stock chart
ax.plot(tickerDf['Date'],tickerDf['Close'],color='black',linewidth=0.6, alpha=1)to plot using the Logarithmic Regression mentioned previously, at least for me, I have a better understanding of where the stock price is and how far can it go from here.
# Plot Logarithmic Regression instead
tickerDf=Logarithmic_regression(tickerDf)
ax=plot_chart(ax, tickerDf)The Logarithmic_regression() routine is the same as before in my previous article, but let me copy it here as well:
def Logarithmic_regression(df):
df = df[df['Close'].notna()]
df['price_y']=np.log(df['Close']) # using natural log of stock price
df['x']=np.arange(len(df)) #fill index x column with 1,2,3...n
try:
b,a =np.polyfit(df['x'],df['price_y'],1)
except Exception as e: # to handle nan value if any
b,a=0,0
df['priceTL']=b*df['x'] + a
df['y-TL']=df['price_y']-df['priceTL']
df['SD']=np.std(df['y-TL'])
df['TL-2SD']=df['priceTL']-2*df['SD']
df['TL-SD']=df['priceTL']-df['SD']
df['TL+2SD']=df['priceTL']+2*df['SD']
df['TL+SD']=df['priceTL']+df['SD']
return dfThe plot_chart () routine will take 2 arguments:
ax — the subplot we are plotting so that it can plot of the exact subplot we are working on for each stock
df — the dataframe of the stock price including the Logarithmic_regression data for us to plot the corridors
it returns the ax after finish plotting the chart so that the main routine can carry on with any beautification or to add more indicators.
def plot_chart(ax,df):
RAINBOWCOLOR1='hotpink'
RAINBOWCOLOR2='orange'
RAINBOWCOLOR3='gold'
RAINBOWCOLOR4='yellowgreen'
RAINBOWCOLOR5='lightgreen'
# plotting stock price on log regression
ax.plot(df['Date'],df['price_y'],color='black',linewidth=0.5)
# plotting stock price on log regression
ax.plot(df['Date'],df['TL+2SD'],color=RAINBOWCOLOR1, linewidth=0.5)
ax.plot(df['Date'],df['TL+SD'],color=RAINBOWCOLOR2, linewidth=0.5)
ax.plot(df['Date'],df['priceTL'],color=RAINBOWCOLOR3,linewidth=0.5)
ax.plot(df['Date'],df['TL-SD'], color=RAINBOWCOLOR4, linewidth=0.5)
ax.plot(df['Date'],df['TL-2SD'],color=RAINBOWCOLOR5, linewidth=0.5)
ax.fill_between(df['Date'],df['TL+2SD'], df['TL+SD'],facecolor=RAINBOWCOLOR2, alpha=0.6,edgecolor=None,linewidth=0)
ax.fill_between(df['Date'],df['TL+SD'], df['priceTL'],facecolor=RAINBOWCOLOR3, alpha=0.6,edgecolor=None,linewidth=0)
ax.fill_between(df['Date'],df['priceTL'], df['TL-SD'],facecolor=RAINBOWCOLOR4, alpha=0.6,edgecolor=None,linewidth=0)
ax.fill_between(df['Date'],df['TL-SD'], df['TL-2SD'],facecolor=RAINBOWCOLOR5, alpha=0.6,edgecolor=None,linewidth=0)
return axIf we run again, here is the result with the same Yahoo screener stock list, but indicated in the Logarithmic regression way.
What I first notice is despite most stock has green indication that it has been risen from yesterday, not all have a uptrend.
These charts do not only give me a better visibility of the trend, but where we are with this trend : be it at the bottom of a rising trend or approaching the top of the corridor in each rainbow Logarithmic Regression indicator which I am expecting the continuation of the firework on one side and need to enter with extra cautious and not get burnt on the other.
Despite META (same as AMAZON) has a surprising surge from yesterday, the indicator is telling me $398 is just at the beginning (lower half of corridor — before the double digit% surge)!!! Please note the price / chart here is showing yesterday close which is before the 15% rise which happened after market on 01 – 02 – 2024.
Disclaimer: I need more study to confirm this uptake is just a beginning, but at least I will look into Facebook’s technical and fundamental before any buying decision if I want to enter into trade today. But at least it gives me one good sign.
For those which are at peak of its corridor, it has the momentum but also a high risk it might turn back down pretty quick. Not for average investor to enter.
Applying other indicator and algo-trade is easy!
Simply change the codes to reflect your indicator will do.
Let me give you one example if to include Exponential Moving Average indiciator in the chart:
def trading_inidicator(ax, df):
df['ema1'] = df['Close'].ewm(span=30, adjust=False).mean()
df['ema2'] = df['Close'].ewm(span=80, adjust=False).mean()
df['ema3'] = df['Close'].ewm(span=100, adjust=False).mean()
df['ema4'] = df['Close'].ewm(span=200, adjust=False).mean()
ax.plot(df['Date'],df['ema1'],color='orange',linewidth=0.6, alpha=1)
ax.plot(df['Date'],df['ema2'],color='blue',linewidth=0.6, alpha=1)
ax.plot(df['Date'],df['ema3'],color='violet',linewidth=0.6, alpha=1)
ax.plot(df['Date'],df['ema4'],color='red',linewidth=0.6, alpha=1)
return axand change back the main routine to plot the trading indicator, I commented out the previous Log Regression plesae note.
# Plot stock chart
ax.plot(tickerDf['Date'],tickerDf['Close'],color='black',linewidth=0.6, alpha=1)
""" Your trading analysis and Plot here """
# tickerDf=Logarithmic_regression(tickerDf)
# ax=plot_chart(ax, tickerDf)
ax=trading_inidicator(ax, tickerDf)This is what you will have as a result with EMA 30, 80, 100, 200 days. Isn’t this much better than reading the yahoo stock list only?
