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 True

Enhancing Stock Picking with Python: Visualizing Trends with Yahoo Finance Data

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