Individual process

Once the screening is complete, you may want to have a look at some stocks within that list. So, the remainder of the notebook is about data visualization at the individual stock level. Input a ticker, for example, ticker = 'FMC':

bm_ticker= '^GSPC'
bm_df = pd.DataFrame()
bm_df[bm_col] = round(yf.download(tickers= bm_ticker,start= start, end = end,interval = "1d",
                 group_by = 'column',auto_adjust = True, prepost = True, 
                 treads = True, proxy = None)['Close'],dgt)
bm_df[ccy_col] = 1
ticker = 'FMC'
lvl = 2 # Try different levels to see
 
df = round(yf.download(tickers= ticker,start= start, end = end,    interval = "1d", group_by = 'column',auto_adjust = True,     prepost = True, treads = True, proxy = None),dgt)
 
df = swings(df,rel = False)
df = regime(df,lvl = 2,rel = False) # Try different lvl values (1-3) to vary absolute sensitivity
df = swings...