Trailing Stop Loss on Pandas dataframe

Question

I am doing some backtesting for some trading strategies on the stock market on a pandas dataframe and I would like to set a trailing stop loss of 1% away from the entered price. If the stock price went up by let's say 5%, the trailing stop loss will move up 5% as well. If the stock price went down, the trailing stop loss will not change. (https://www.investopedia.com/terms/t/trailingstop.asp)

I have this table which shows my signal to enter and the exit column will show a value of 1 if the price goes below the trailing stop loss price, which means the trade is exited.

This is the table I have so far:

date           price      entry_signal      
30/06/2018     95              0                
01/07/2018     100             1                
02/07/2018     103             0                
03/07/2018     105             0                
04/07/2018     104.50          0                
05/07/2018     101             0                

I would like to have a column showing what is the trailing stop loss at every date. The trailing stop loss is first set as 99% of the price on 01/07/2018 when the enter_signal = 1, where trade is executed on this date.

When the price moves up by y%, the trailing stop loss will move up by y% as well. However if the price goes down, the trailing stop loss will not change from its last value.

When the price <= trailing stop loss, the trade is exited and there will be an exit_signal of 1...

I am currently stuck at not having the trailing stop loss to move down by y% if the price move down by y% as well....

Desired table outcome:

date           price      trailing stop loss      entry_signal      exit_signal
30/06/2018     95              NULL                     0                0
01/07/2018     100             99                       1                0
02/07/2018     103             101.97                   0                0
03/07/2018     105             103.95                   0                0
04/07/2018     104.50          103.95                   0                0
05/07/2018     101             103.95                   0                1

Table I have obtained:

date           price      trailing stop loss      entry_signal      
30/06/2018     95              NULL                     0                
01/07/2018     100             99                       1                
02/07/2018     103             101.97                   0                
03/07/2018     105             103.95                   0                
04/07/2018     104.50          103.455                  0                
05/07/2018     101             99.99                    0                

Answer 1

Just take 99% of the cumulative maximum and compare it with the current price:

df = pd.DataFrame({"price":[95,100,103,105,104.5,101]}) #create price array
df['highest'] = df.cummax() #take the cumulative max
df['trailingstop'] = df['highest']*0.99 #subtract 1% of the max
df['exit_signal'] = df['price'] < df['trailingstop'] #generate exit signal


Out[1]: 
   price  highest  trailingstop  exit_signal
0   95.0     95.0         94.05        False
1  100.0    100.0         99.00        False
2  103.0    103.0        101.97        False
3  105.0    105.0        103.95        False
4  104.5    105.0        103.95        False
5  101.0    105.0        103.95         True

Answer 2

Hard question involved cummax and pct_change+ clip_lower + cumprod

s=df.loc[df.entry_signal.cummax().astype(bool),'price'].pct_change().add(1).fillna(1)

df['trailing stop loss']=s.clip_lower(1).cumprod()*99
df['exit_signal']=(df['trailing stop loss']>df['price']).astype(int)
df
Out[114]: 
         date  price  entry_signal  trailing stop loss  exit_signal
0  30/06/2018   95.0             0                 NaN            0
1  01/07/2018  100.0             1               99.00            0
2  02/07/2018  103.0             0              101.97            0
3  03/07/2018  105.0             0              103.95            0
4  04/07/2018  104.5             0              103.95            0
5  05/07/2018  101.0             0              103.95            1