@knightfallShashy

Big frontend push this session. I finished building out all five core pages: Home, Markets, Watchlist, About, and Contact. I also wired up a shared navbar with real routing. The ticker research input now lives on the Markets page instead of Home, freeing up the homepage for additional details. Built out the Home page hero to match a Bloomberg Terminal-inspired design: a dark section with a faint SVG chart-line pattern behind the text, and an “Explore Markets” button that actually routes to /markets. Also I made a scrollable charts section In the top. Overall it was filled with a lot of complicated Reflex design and painful bugs. I’m going to connect the backend and frontend soon though.

Wrapped up the last piece of the core DCF math, then started the actual website since real cash flow data is still held behind the FMP Starter upgrade. I built the WACC math which ties into the Dcf calculation. Additionally, I have started my frontend by installing reflex and starting to work on the site itself. It’s still in it’s starting stages but took a lot of bug fixing.

Picked up where I left off with two small FMP cleanup items, then spent most of this session building the actual valuation math. Found out the $19/mo Starter tier does not extend that 5-year cap, it only unlocks the balance sheet/cash flow endpoints and more bandwidth. Every function got hand verified against math I worked out on paper before trusting the code, there is nothing that went in without me being able to explain why the numbers came out the way they did. I have much more screenshots, but didn’t have the time to paste them.

I built out functions to pull income statements, balance sheets, and cash flow statements straight from FMP, following the same pattern as my earlier company profile integration. Income statement worked immediately with real multi-year data for Apple. Balance sheet and cash flow both came back with a 404 though. Apparently, my free-tier FMP account likely doesn’t include full financial statement access, based on comparing their pricing page (that data seems to be behind their $19/mo Starter plan). Rather than paying right away, I’m holding off and treating this as a “figure out before launch” decision.

I finally built out my database session and my first company table model. Aditionally I also wrote a POST/ companies endpoint that takes data, validates it, and saves it to the database. Lastly after so much fixing I finally got a clean 200 response back with Microsoft’s data and a real database ID, so the full loop — request → validate → save → confirm — is working end to end now.

This is my first devlog for Investie. So far I finished installing all my requirements, I made a Fast API server with a few words on it. Additionally, I ended up getting a bunch of important files made that are needed for the project. Lastly, I signed up with SupaBase to launch my site through their network and use it for API key connections. The main part of today was debugging, which took a painfully long time.

I officially finished the Dist file. with everything compiled in it. Additionally, I’m done writing the README and pushed everything to Git.

I finished compiling a dist file to group everything into 1 file and push it to Github.

I finally finished the README and finished the print statements for the FICO scores distributions. Additionally, I found the default probability and the log- likelihood score.

Im finally done with the actual coding part itself, it has taken such a long time trying to debug and figure out the formulas. I’m about to get started on the README now.

I have run into a bug, where even if I fix the python syntax it still doesn’t run properly. I am trying everything in my arsenal to get this to work.

I figured out how to get the FICO scores sorted and am currently working on implementing the log functions into my code.

My goal is FICO score quantization, which means converting continuous FICO scores (300–850) into discrete buckets/ratings for a machine learning model that predicts mortgage default probability.

Built a credit risk model for JPMorgan Chase Quantitative Research Task 3. Used logistic regression in Python (scikit-learn) to predict the probability of default (PD) for 10,000 loan borrowers. Split the data 80/20 for training and testing, then calculated expected loss per loan using the formula: PD × Loan Amount × 0.90, assuming a 10% recovery rate.

I built a natural gas contract pricing model in Python as part of the JPMorgan Chase Quantitative Research Virtual Experience (Task 2). The model calculates the fair value of a storage contract where a client buys gas in summer, stores it, and sells in winter. It accounts for all cash flows: purchase cost, sale revenue, monthly storage fees, and injection/withdrawal costs.

I built a natural gas price estimator in Python for the JPMorgan Chase Quantitative Research Forage simulation. Loaded and parsed historical monthly price data, I visualized the time series to identify seasonal trends (winter peaks, summer dips), and I built a function that uses linear interpolation for historical dates and linear regression forecasting for future dates.