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When technology meets strategy - Enhancing Investment Analysis with Google Gemini

Writer's picture: Henri CaronHenri Caron

Disclaimer: This content is for informational purposes only and does not constitute investment advice. Please consult a financial advisor before making any investment decisions.


The world of investing evolves. Google Gemini, an advanced AI tool designed to assist in various tasks, has recently helped me elevate my investment analysis. By refining the code I use to analyze assets, Gemini has made my processes faster and more stable, enhancing the overall quality and reliability of my work.


Data Gathering

The first step in my analysis involves gathering data, which is the foundation of everything. With Gemini's help, I’ve optimized my code to include all the 6,000+ stocks and cryptocurrencies available on eToro. For example, I have added more markets (ie. Toronto) and I now analyze Tesla stock alongside cryptocurrencies like Bitcoin and Ethereum. This expanded capability gives me a much broader and more detailed view of the market.


Data Cleaning

Next, I clean up the data. Raw data often contains errors or extreme values, such as a stock showing a 10,000% return, which is likely a glitch. Improved code lets me quickly identify and remove these outliers. It’s like fine-tuning a recipe: if one ingredient mistakenly calls for “500 teaspoons of salt,” the result would be ruined. Cleaning the data ensures that what remains is usable and relevant.



Scatter plot highlighting an outlier asset with flawed data, marked for exclusion from the analysis due to its extreme return and volatility values.
Scatter plot highlighting an outlier asset with flawed data, marked for exclusion from the analysis due to its extreme return and volatility values.
Scatter plot showing assets characterized by their annualized return and volatility, with outliers removed based on mean and variance. Red dots represent assets removed by both criteria, orange dots by mean, purple by variance, and blue for assets kept for analysis.
Scatter plot showing assets characterized by their annualized return and volatility, with outliers removed based on mean and variance. Red dots represent assets removed by both criteria, orange dots by mean, purple by variance, and blue for assets kept for analysis.
Scatter plot of assets displaying annualized return versus volatility. Blue dots represent assets selected for further analysis, while other colors indicate removed assets due to lower mean or variance.
Scatter plot of assets displaying annualized return versus volatility. Blue dots represent assets selected for further analysis, while other colors indicate removed assets due to lower mean or variance.

Portfolio Building

After cleaning, I build portfolios using an optimizing model based on modern portfolio theory. This step helps me balance risk and reward. For instance, it might suggest combining high-growth stocks like Apple with more stable ones like Coca-Cola to create a diversified portfolio. While Gemini hasn’t directly sped up this optimization process, the cleaner datasets I produce with its help allow the process to run much more smoothly and effectively.

The blue line shows the line of optimized portfolios that are possible to obtain with the assets represented in blue.
The blue line shows the line of optimized portfolios that are possible to obtain with the assets represented in blue.


Impact of Improved Methods

These improved methods have made a significant impact. I can now analyze all the stocks and cryptocurrencies available on eToro, ensuring my analysis is comprehensive. The datasets I work with are more accurate and reliable, and the optimization process is smoother, resulting in better portfolio recommendations.


Results and Future Goals

This approach has already delivered concrete results. My Couguar portfolio on eToro achieved a 41% return in 2024. By continuing to refine my methods with the support of Google Gemini, I’m aiming for even better outcomes in the future.

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