21 Nov 2022
Building an Intelligent Investment Advisory App for a leading US Fintech company
Executive Summary
The customer is a recently founded fintech company based out of the USA which is both an investment manager and a SaaS provider. They intend to build a product to assist financial analysts at investment firms to assist in trading stocks. The goal of this product is to provide customers a managed portfolio of stocks which are predicted to give the best returns for the foreseeable future. The returns for each company would be predicted by using a Machine Learning algorithm.
Problem Statement
The client needed a product/solution to advice financial analysts for investing in stocks in the US market, the predicted best-performing stocks from the Russell 1000 index, in order to maximize their customer’s profits by building portfolios for investing in stocks from the index. businesses.
Business Requirements
End Objective
To provide an application that can predict stock returns for each stock against the performance of the Russell 1000 index and creating a portfolio of the predicted best-performing stocks in the index to provide insight to financial analysts to buy/sell stocks based on predicted performance.
Key Requirements
Historical Data for the stocks in the index
Understanding of securities and options.
A user interface for visual representation of calculated portfolios and the metrics associated with the generated portfolios
Impact and involvement of stakeholders
Large investment management firms
AI-based forecasting of alpha also helps smaller or non-specialized institutions, individual investors.
Solution Approach
Our Solution Structure
All Data imported from various Data sourcing companies are stored in a framework using resources in a cloud infrastructure. In this case, we extensively use Amazon Web Services (AWS)
A custom-built Machine Learning model using Long Short Term Memory (LSTM) Neural networks, which predicts the returns of a given stock within a stipulated time frame.
A service to regularly train the existing Models based on daily updated data
A custom-built selection Algorithm for building a portfolio by judging the predicted returns from the models based on several parameters.
A user interface for viewing the results
We used TensorFlow and Keras libraries to develop an LSTM Neural network factoring in the following inputs:
Historical Prices of the stocks, their history of splits, spinoffs, etc…
External variables (For example. Oil Prices, Gold Prices, etc…)
CBOE options data (Stock Volatilities)
Custom created Variables that track the actual performance of a company without relying solely on the prices data.
Among others…
Solution Dynamics and Interactions
Financial prediction problems are a difficult type of predictive models. The complexity comes from the dependency between the factors vary over time. To tackle this problem we need a recurrent neural network, which can learn the pattern in which the stock market moves and predict based on that.
We have more than 50 input factors which are fed into our model. LSTM model will be trained in high GPU accelerated machines. We predict a top excess return over Russell 1000. Based on the excess return prediction portfolio will be generated. The portfolio has top N (adjustable by users as per preference) companies which can overperform with respect to the index.
User Interface
We built a web interface which creates and displays a portfolio for different time periods which are limited to the backtesting period.
The user can change the financial index (benchmark) with which they want to compare varied stock’s performance.
The user also has the option to generate graphical results which assist in visually inspecting Portfolio performance.
Technology Stack
Tensorflow / Keras (Python)
Django Framework (Back-end)
ReactJs (Front-end)
Business Outcomes
We helped our client to capitalize on the state of the art AI techniques to predict the alpha in the market.
  • Our solution is helping the customer grow as a massive institutional traders.
  • Safeguard retail and small institutional traders.
  • In a market crash situation, it avoids a big loss.
  • The graphical representation will help traders to understand how a particular stock may move in the future before they invest in it.