Creating several overlapping pdf with a stock chart for printing and analysis (DRAFT edition)

gistfile1.txt

import json
from tqdm import tqdm
from bs4 import BeautifulSoup
import requests
import pandas_ta as ta
import yfinance as yf
import pandas as pd
import numpy as np

import mplfinance as mpf
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
from matplotlib.backends.backend_pdf import PdfPages
from zigzag import *



STOCK = 'TSLA'
INDEX = '^GSPC' # S&P500

bar_in_page = 250
shift = 35

# get stock data 
def get_ohlc (stock, period = 'max', interval = '1d'):
    df_ohlc = yf.Ticker(stock.upper()).history(actions = False, period = period, interval = interval)
    return df_ohlc


df = get_ohlc(STOCK) # main DataFrame for join everything
info = yf.Ticker(STOCK).info
df_idx = get_ohlc(INDEX)

df=df.join(df_idx['Close'], rsuffix='_idx')


# add TA columns 

df.ta.sma(length=50,append=True)
df.ta.sma(length=200,append=True)
df.ta.sma(length=150,append=True)
df.ta.ema(length=21,append=True)
df['VOL_50'] = df.ta.sma(close=df['volume'], length=50)

# add RS-line column

stock_close = df['close']
ind_close = df['Close_idx']
df['rs_line'] = stock_close/ind_close*100 * stock_close.shift(60)/(stock_close/ind_close *100).shift(60)*0.68  # 0.68 for placing like in  IBD chart


# add Max and Min extremum for price labels

treshold = 0.1 # % of changing price to count as extremum

df['max']=df.high[peak_valley_pivots(np.array(df.high), treshold, -treshold) == 1]
df['min']=df.low[peak_valley_pivots(np.array(df.low), treshold, -treshold) == -1]


# last OHLC data for titles
o = df.open[-1]
h = df.high[-1]
l = df.low[-1]
c = df.close[-1]
v = df.volume[-1]
chg = (df.close[-1]/df.close[-2]-1)*100

# add space to the right side of chart
dfpad = df.tail(round(bar_in_page/50)).copy()
dfpad.loc[:,:] = float('nan')
newdf = df.append(dfpad)
df = newdf



# todo - get financial info like EPS and Sales

def get_macro_info(stock="AAPL", period='q'):
    pass

# make chunks for export in multiple pages pdf, I guess there is the way to do the same better 

iter_list = list(range(len(df)))

chunks = [iter_list[i:i + bar_in_page] for i in range(0, len(iter_list), bar_in_page-shift)] 
pages = [(x[0],x[-1]+1) for x in chunks] # pages[n][0] - slice start, pages[n][1] - slice end

# styling chart

mc = mpf.make_marketcolors(up='#2A3FE5', down='#DB39AD', inherit=True)

base_style = {  'axes.titlesize':       5,
                'axes.labelsize':       5,
                'lines.linewidth':      3,
                'lines.markersize':     4,
                'ytick.left':           False,
                'ytick.right':          True,
                'ytick.labelleft':      False,   
                'ytick.labelright':     True, 
                'xtick.labelsize':      5,
                'ytick.labelsize':      5,
                'axes.linewidth':         0.8,
                'savefig.pad_inches': 0.1,
                'savefig.bbox': 'tight',
                'grid.alpha':           0.2}

ibd = mpf.make_mpf_style(marketcolors=mc, mavcolors=['green', 'red', 'black', 'blue'], y_on_right=True, rc=base_style)

### !!! temporary replace to chunks loop
df_slice = df[-550:]

# ======== starting ploting ==================================================


# making grid of axis
egrid = (21,29)

fig = mpf.figure(style=ibd, figsize=(11, 8))
ax1 = plt.subplot2grid(egrid,(1,0),colspan=29,rowspan=16)
ax3 = plt.subplot2grid(egrid,(0,0),colspan=29,rowspan=1)
ax2 = plt.subplot2grid(egrid,(17,0),colspan=29,rowspan=4,sharex=ax1)

# remove gaps among axis panels
fig.tight_layout(h_pad= -1.6)

#  Set locator intervals


lim_bottom =min(df_slice['rs_line'].min(), df_slice['SMA_200'].min())
lim_top =  max(df_slice['rs_line'].max(), df_slice['SMA_200'].max(),df_slice['close'].max())


#  Enable minors ticks visible:

ax1.minorticks_on()

ax1.grid(which='major',color='k')
ax1.grid(which='minor',color='gray')
ax2.grid(which='major',color='gray')



ax2.tick_params(axis='x', which='major', pad = 8)

ax1.tick_params(axis='x', which='both',labelbottom= False, labeltop=False )
ax3.tick_params(which = 'both',labelbottom= False, labeltop=False, labelright = False, bottom=False, top=False, right = False)

base = len(df_slice)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%m'))
ax1.xaxis.set_major_locator(mticker.IndexLocator(base=base/10, offset=0))
ax1.xaxis.set_minor_formatter(mdates.DateFormatter('%d'))
ax1.xaxis.set_minor_locator(mticker.IndexLocator(base=base/50, offset=0))


# making y-axis locator align with prices scale

maxprice = df_slice.high.max()
if maxprice > 100:
    locator_size = 100
elif maxprice < 10:
    locator_size = 1
else:
    locator_size = 10

#ax2.ticklabel_format(axis='y',style='plain')

ax1.yaxis.set_major_locator(mticker.MultipleLocator(locator_size))
ax1.yaxis.set_minor_locator(mticker.MultipleLocator(locator_size/4))
#ax2.yaxis.set_major_formatter(mticker.MultipleLocator(df_slice['volume'].max()/5))
#ax2.yaxis.set_major_locator(mticker.MultipleLocator(df_slice['volume'].max()/5))


# additional lines
#sp500 = mpf.make_addplot( df_slice['Close_idx'], color='black', width=0.9, panel=0, title=f'{STOCK.upper()} {df_slice.index[0]} - {df_slice.index[-1]}', ax=ax3)

sp = mpf.make_addplot( df_slice['Close_idx']*1/9+maxprice/1.7, color='black', width=0.9, ax=ax1)



vol50 = mpf.make_addplot(
    df_slice['VOL_50'], panel=2, color='red', width=0.6, ax=ax2)
rs_line = mpf.make_addplot(
    df_slice['rs_line'], ax=ax1, color='blue', width=0.6, alpha=0.75, panel=1)

sma200 = mpf.make_addplot(
    df_slice['SMA_200'], ax=ax1,color='black', width=0.8, panel=1)
sma50 = mpf.make_addplot(
    df_slice['SMA_50'], ax=ax1,color='red', width=0.6, panel=1)
sma150 = mpf.make_addplot(
   df_slice['SMA_150'], ax=ax1,color='gray', width=0.2, alpha=0.8, panel=1)
ema21 = mpf.make_addplot(
   df_slice['EMA_21'], ax=ax1,color='green', width=0.3, panel=1)

# text adding

kwargs = dict(horizontalalignment='center', color='#000000', fontsize = 5, backgroundcolor = 'white', 
              bbox=dict(boxstyle='square', fc='white', ec='none', pad=0)) 

#ax1.text(mdates.date2num(df_slice.index[80]), df_slice.high.iloc[80]+text_pad, "HELLO", verticalalignment='top',  **kwargs)

last_coord = -1-(round(bar_in_page/50)) 

#ax1.text(245, df_slice['rs_line'][last_coord]-5, "RS", color ='blue', verticalalignment='bottom', fontsize=7, alpha = 0.6)

# add price labels above/below extremum bars

price_pad = 15

for i in range(len(df_slice)):
    if not(np.isnan(df_slice['max'].iloc[i])):
        ax1.text(i+1, df_slice['max'].iloc[i]+price_pad, np.round(df_slice['max'].iloc[i],2), **kwargs, verticalalignment='bottom')
    if not(np.isnan(df_slice['min'].iloc[i])):
        ax1.text(i+1, df_slice['min'].iloc[i]-price_pad, np.round(df_slice['min'].iloc[i],2), **kwargs, verticalalignment='top')
        
    

mpf.plot(df_slice, ax=ax1, volume=ax2,addplot=[sp, rs_line, sma200, sma150, sma50, ema21, vol50], datetime_format="%b'%y",tight_layout=True, xrotation=0,
    scale_width_adjustment=dict(volume=0.2),ylim=(lim_bottom*0.1,lim_top*1.1), update_width_config=dict(ohlc_ticksize=0.5))

# scale_width_adjustment=dict(volume=0.2), addplot=[sp500, rs_line, sma200, sma150, sma50, ema20, vol50],
# datetime_format="%b'20",tight_layout=True, xrotation=0, ylim=(lim_bottom*0.1,lim_top*1.1),update_width_config=dict(ohlc_linewidth=0.9,ohlc_ticksize=0.4)

ax3.text(0.01, 0.6, f"{info['shortName']} /{info['symbol']}/     MarketCap: {np.round(info['marketCap']/1000000000,2)}B     Shares Outstanding: {np.round(info['sharesOutstanding']/1000000,2)}M", fontsize=8)
ax3.text(0.6, 0.2, f"{info['sector']}  -  {info['industry']}" , fontsize=8)
ax3.text(0.85, 0.6, f"{df_slice.index[0].strftime('%d %b %Y')} - {df_slice.index[-1].strftime('%d %b %Y')}" , fontsize=8)
ax3.text(0.01, 0.2, f"Last open: {o} high: {h} low: {l} close: {c} %chg: {np.round(chg,2)}    Vol: {np.round(v/1000000,2)}M", fontsize=8)


#enable volume log scale
ax2.set_yscale('symlog')
ax2.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, pos: str(np.round(x/1000000,1))+'M'))
ax2.yaxis.set_major_locator(mticker.LogLocator(base=5.0))


# add 100 to bottom of y-axis to put some useful info later
ylimit_ax1 = ax1.get_ylim()
ax1.set_ylim(ymin = ylimit_ax1[0]-100)



fig.savefig('figure.pdf')

Great work - Thank you
I am unable to use Nasdaq symbol '^IXIC' with TQQQ symbol. Nothing shows up on the top info bar.
Am I using the wrong index?