Making Machine Learning accessible to individual investors with Kupala-nich
M 917 536 3378maksim_kozyarchuk@yahoo.com
Making Machine Learning accessible to individual investors with Kupala-nich M 917 536 3378 maksim_kozyarchuk@yahoo.com |
You’ve heard the buzz about AI, and you’ve likely spent time with tools like ChatGPT, finding them useful for general queries and assistance. However, when it comes to analyzing your portfolio or uncovering investment opportunities, they fall short. To bridge this gap, Kupal-nich platform brings Machine Learning (ML) models tailored for financial applications within the reach of a classically trained financial analyst and individual investor.
There are many ML models and frameworks used across industries like healthcare, retail, finance, and manufacturing. Typically, applying these models requires scientists skilled in data engineering and model selection. AutoML frameworks recently emerged to simplify ML access by bundling various algorithms into a single package with a low-code API. This has lowered the entry barrier for users with basic Python skills and a foundational grasp of ML workflows, allowing them to train, select, and apply models more easily. A leading open-source AutoML framework, PyCaret, is powerful but tailored to the research community—users familiar with Jupyter notebooks and standard ML steps like data preprocessing, feature engineering, model selection, and hyperparameter tuning. This leaves a gap for financial analysts who primarily work with tools like Excel and are accustomed to spreadsheet formats.
Kupala-nich bridges this gap by making PyCaret and AutoML technology accessible to traditional financial analysts. It does this by integrating rich datasets on broadly held companies with Excel-like tools for data pivoting, filtering, sorting, and custom column derivation, enabling deeper insights and analysis. Users can visually explore and identify columns of interest before kicking off model training with a simple button click. It also allows users to upload their proprietary Excel sheets and datasets.
Behind the scenes, Kupala-nich uses PyCaret’s AutoML features to train multiple models and produce predictive analysis based on the best-performing model. This functionality is delivered via an AWS Lambda deployment. While AWS Lambda might seem like an unconventional choice for deploying PyCaret due to its package size (~1.5 GB), it has proven to be highly cost-effective for typical analysis workloads associated with S&P 500 datasets, with 10 runs costing only around $0.01. For users who require fewer than a thousand runs daily, Lambda is a far more economical solution compared to options like AWS Fargate. This calculus may shift for more intensive workloads, such as time series analysis and backtesting on large datasets.
If you're interested in learning more, check out the Financial Analysis tab on Kupala-Nich or reach out to me at maksim.kozyarchuk@gmail.com.
You’ve heard the buzz about AI, and you’ve likely spent time with tools like ChatGPT, finding them useful for general queries and assistance. However, when it comes to analyzing your portfolio or uncovering investment opportunities, they fall short. To bridge this gap, Kupal-nich platform brings Machine Learning (ML) models tailored for financial applications within the reach of a classically trained financial analyst and individual investor.
There are many ML models and frameworks used across industries like healthcare, retail, finance, and manufacturing. Typically, applying these models requires scientists skilled in data engineering and model selection. AutoML frameworks recently emerged to simplify ML access by bundling various algorithms into a single package with a low-code API. This has lowered the entry barrier for users with basic Python skills and a foundational grasp of ML workflows, allowing them to train, select, and apply models more easily. A leading open-source AutoML framework, PyCaret, is powerful but tailored to the research community—users familiar with Jupyter notebooks and standard ML steps like data preprocessing, feature engineering, model selection, and hyperparameter tuning. This leaves a gap for financial analysts who primarily work with tools like Excel and are accustomed to spreadsheet formats.
Kupala-nich bridges this gap by making PyCaret and AutoML technology accessible to traditional financial analysts. It does this by integrating rich datasets on broadly held companies with Excel-like tools for data pivoting, filtering, sorting, and custom column derivation, enabling deeper insights and analysis. Users can visually explore and identify columns of interest before kicking off model training with a simple button click. It also allows users to upload their proprietary Excel sheets and datasets.
Behind the scenes, Kupala-nich uses PyCaret’s AutoML features to train multiple models and produce predictive analysis based on the best-performing model. This functionality is delivered via an AWS Lambda deployment. While AWS Lambda might seem like an unconventional choice for deploying PyCaret due to its package size (~1.5 GB), it has proven to be highly cost-effective for typical analysis workloads associated with S&P 500 datasets, with 10 runs costing only around $0.01. For users who require fewer than a thousand runs daily, Lambda is a far more economical solution compared to options like AWS Fargate. This calculus may shift for more intensive workloads, such as time series analysis and backtesting on large datasets.
If you're interested in learning more, check out the Financial Analysis tab on Kupala-Nich or reach out to me at maksim.kozyarchuk@gmail.com. |
1 comment:
👍🏾 Maks - shall check this out - thanks
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