Hey AlMoJo,

ChatGPT is not trained to program a strategy in QC in most cases the code will be wrong and you will spend more time trying to correct the code rather than writing it on your own. 

I can name a few things that are missing for it to work:

1. You do not add the equity to your system you just name the symbols. 

for x in self.assets:
  self.AddEquity(x, Resolution.Daily).Symbol

2. The program does not enter any function you will have to add this:

 self.Schedule.On(self.DateRules.EveryDay("SPY"),
                 self.TimeRules.AfterMarketOpen("SPY", 1),
                 self.EveryDayAfterMarketOpen)

3. You will have to use the history request to feed your indicators:

#Initialize
for x in self.assets:
            self.AddEquity(x, Resolution.Daily).Symbol
            self.ma60 = self.SMA(x, 60)
            self.ma150 = self.SMA(x, 150)
            self.rsi = self.RSI(x, 30)
            
#GetSignal
if not df.empty:
   for time, price in df.loc[symbol]["close"].iteritems():
   #UPDATE INDICATORS FIRST HERE -- YOU WILL FIND A FEW COMMENTS ON HOW THIS WORKS IN THE FORUM
   df.loc[time, 'ma60'] = self.ma60.Current.Value
   df.loc[time, 'ma150'] = self.ma150.Current.Value
   df.loc[time, 'rsi30'] = self.rsi.Current.Value
   
   
   #Make sure that the Indicators correspond to the right symbols and dont mix up
   		
+ Expand
- Collapse

4. At the end you want to probably know what the values will be so you could try to plot them:

if len(df) >= self.min_data:
            X = df['open', 'high', 'low', 'close', 'volume', 'ma60', 'ma150', 'rsi30']
            X = (X - X.mean()) / X.std()
            X = X.values
            regressor = LogisticRegression(random_state=0, solver='lbfgs', max_iter=100)
            signal = np.where(df['close'].shift(-1) > df['close'], 1, -1)
            X_train, X_test, y_train, y_test = train_test_split(X, signal, test_size=0.2, random_state=1990)
            regressor.fit(X_train, y_train)
            self.Plot("Signal", "Value", np.sign(regressor.predict(X)[-1]))
            return np.sign(regressor.predict(X)[-1])
        else:
            return 0
            self.Plot("Signal", "Value", 0)

Note: I did not take a look at the CreateStopLossAndTakeProfitOrders function. Also not sure why you created a rolling window for the portfolio value you can take a look at it in the backtest results.

Hope it helps ;)