Good evening GQ,

Today I would like to introduce you to my Nasdaq100 Leveraged Long-Term Trading System. The whole thing is by and large an AI model which is based on the $EQQQ (-0,72 %) the $TQQQ , $SQQQ or cash.

Origin

The idea for the model was born in December after a conversation with @Epi about his GTAA model which I have been following for some time. The first idea at that time was to buy the leveraged Nasdaq100 by comparing the 50-SMA and 200-SMA. $TQQQ to buy the leveraged one. After various backtests on portfolio visualizers and unsuccessful attempts in Excel to find a pattern within the technical indicators, the idea of my own AI model was born.

The model

The LTTS model basically consists of 2 parts in Python. The first part uses an ensemble of 3 AI models to forecast the course of the Nasdaq100 over the next few days using various technical indicators. The history of SMA, RSI, MACD, ROC, VIX or various volume parameters back to 2010 are used. The output of the ensemble is a trading signal and the current model confidence.

The second part of the model consists of a kind of optimization loop that uses the Bayesian optimization model to try to optimize several indicators such as the buy thresholds for $TQQQ , $SQQQ , cash, smoothing parameters, interaction of trading signal with confidence, ensemble agreement and other parameters through backtests. This means that the model itself carries out backtests with the signals from the forecast model over various time ranges and settings in order to optimize performance. The AI is rewarded with high returns or low drawdowns, while deductions are made for an excessively high number of trades (long term model) or low sharp ratios.

Development

When creating the concept, it quickly became clear that the model should run in Python on Google Colab. Since I myself work as an engineer in the development department of a semiconductor company, I am basically familiar with models and programming, but in much lower-level programming languages. Since my Python knowledge is limited, I decided to develop the model with the help of LLM-AI models (mainly Claude Sonnet 3.5, individually also ChatGPT and Perplex) and to optimize the code. The experience of using AI on a daily basis at work has been confirmed time and again. The AI has suggested code that I would never have managed to program in this way, but you have to check each line individually and debug very carefully whether everything has been understood correctly and the code does what it is supposed to do. In total, about 80 hours have gone into the development in the last few months, the final training of the models (on my iMac from 2012 - Colab has a limited runtime) is about 28 hours and will be repeated every 2-3 weeks. The daily calculation of the current signals has a runtime of approx. 1 minute on Colab.

Outlook

Even though the backtests have produced very good results, the model still has to prove itself, which is why there is currently still a small investment amount in the new GQ portfolio. I will share my experiences with the model with you over the next few months. In theory, the model can be trained (and traded) with any asset, but currently only the Nasdaq with the corresponding levers is planned.

Current LTTS signal: Cash (will be entered accordingly in the GQ portfolio in order to be able to compare with the market)

Best regards

Your Internet Explorer