AIM - AI powered Investment Mentor

Boost portfolio performance by 25% with our GenAI-powered Investment Mentor. Optimize asset allocation using hedge fund strategies, real-time news & technical analysis, & investor sentiment insights.

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Inspiration

As a client of financial advisors, I’ve experienced firsthand the frustration of high advisor fees that don’t reflect their actual performance, a generic approach to managing my investments, and slow responses to critical market changes. It’s discouraging when fees aren’t tied to results, when the advice does not include fast moving, but long-term reliable stocks (like Nvidia), and when my portfolio can’t quickly adapt to events like economic downturns or geopolitical crises.

What it does

AIM is an AI Hedge Fund Trader agent that analyzes client's portfolio and offers contextual advice for better returns, and recommend strategies based on current events, investor sentiments from news, future outlook and cash flow changes, leveraging options flow data.

This enables investors to price in risks and make data driven investment decisions and respond quickly.

We also simplify the complex financial investment jargons for retail clients, provide automated explanation behind the recommended strategy.

How we built it

Extensively leveraged 3rd party market data sources like Yahoo Finance, MarketWatch, NASDAQ, combined with latest news articles as they appear in Google Search that could potentially impact the portfolio. The proprietary algorithm enables to process this data (structured and unstructured) in integrated manner and provide hyper contextual recommendations like hedge fund trader.

Challenges we ran into

  • Understanding the market data attributes from various data sources (currently free)
  • Complex trade indicators like option flow data, MACD, Bollinger indicators, VWAP for each of the stock using Python with volumes in GBs.
  • Automatically parsing URLs for the latest news articles and identifying investor sentiments in scalable manner, while reducing the need for API execution to minimize cost.
  • Performance requirements when the client portfolio exceeds 50+ stock symbols.

Accomplishments that we're proud of

Learnt Databricks and aws in 2 days and developed the solution.

While not comprehensive; it gave us significant edge in terms of development, and actual outcome.

We reviewed and we have applied strategy for our personal portfolio and we could see significant gain.

What we learned

Financial data sourcing from free sources have limitations.

Being able to create a quick angular app interacting with lambda running serverless function

What's next for AIM - AI powered Investment

Real time api to analyze large portfolio and provide recommendations, by introducing parallel processing.

Integration with brokerage software for trading based on recommendations

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