## Inspiration

We're all told that stocks are a good way to diversify our investments, but taking the leap into trading stocks is daunting. How do I open a brokerage account? What stocks should I invest in? How can one track their investments? We learned that we were not alone in our apprehensions, and that this problem is even worse in other countries. For example, in Indonesia (Scott's home country), only 0.3% of the population invests in the stock market.

A lack of active retail investor community in the domestic stock market is very problematic. Investment in the stock markets is one of the most important factors that contribute to the economic growth of a country. That is the problem we set out to address. In addition, the ability to invest one's savings can help people and families around the world grow their wealth -- we decided to create a product that makes it easy for those people to make informed, strategic investment decisions, wrapped up in a friendly, conversational interface.

## What It Does

PocketAnalyst is a Facebook messenger and Telegram chatbot that puts the brain of a financial analyst into your pockets, a buddy to help you navigate the investment world with the tap of your keyboard. Considering that two billion people around the world are unbanked, yet many of them have access to cell/smart phones, we see this as a big opportunity to push towards shaping the world into a more egalitarian future.

**Key features:**

* A bespoke investment strategy based on how much risk users opt to take on, based on a short onboarding questionnaire, powered by several AI models and data from Goldman Sachs and Blackrock.
* In-chat brokerage account registration process powered DocuSign's API.
* Stock purchase recommendations based on AI-powered technical analysis, sentiment analysis, and fundamental analysis based on data from Goldman Sachs' API, GIR data set, and IEXFinance.
* Pro-active warning against the purchase of a high-risk and high-beta assets for investors with low risk-tolerance powered by BlackRock's API.
* Beautiful, customized stock status updates, sent straight to users through your messaging platform of choice.
* Well-designed data visualizations for users' stock portfolios.
* In-message trade execution using your brokerage account (proof-of-concept for now, obviously)

## How We Built it

We used multiple LSTM neural networks to conduct both technical analysis on features of stocks and sentiment analysis on news related to particular companies

We used Goldman Sachs' GIR dataset and the Marquee API to conduct fundamental analysis. In addition, we used some of their data in verifying another one of our machine learning models. Goldman Sachs' data also proved invaluable for the creation of customized stock status "cards", sent through messenger.

We used Google Cloud Platform extensively. DialogFlow powered our user-friendly, conversational chatbot. We also utilized GCP's computer engine to help train some of our deep learning models. Various other features, such as the app engine and serverless cloud functions were used for experimentation and testing.

We also integrated with Blackrock's APIs, primarily for analyzing users' portfolios and calculating the risk score.

We used DocuSign to assist with the paperwork related to brokerage account registration.

## Future Viability

We see a clear path towards making PocketAnalyst a sustainable product that makes a real difference in its users' lives. We see our product as one that will work well in partnership with other businesses, especially brokerage firms, similar to what CreditKarma does with credit card companies. We believe that giving consumers access to a free chatbot to help them invest will make their investment experiences easier, while also freeing up time in financial advisors' days.

## Challenges We Ran Into

Picking the correct parameters/hyperparameters and discerning how our machine learning algorithms will make recommendations in different cases.

Finding the best way to onboard new users and provide a fully-featured experience entirely through conversation with a chatbot.

Figuring out how to get this done, despite us not having access to a consistent internet connection (still love ya tho Cal :D). Still, this hampered our progress on a more-ambitious IOT (w/ google assistant) stretch goal. Oh, well :)

## Accomplishments That We Are Proud Of

We are proud of our decision in combining various Machine Learning techniques in combination with Goldman Sachs' Marquee API (and their global investment research dataset) to create a product that can provide real benefit to people. We're proud of what we created over the past thirty-six hours, and we're proud of everything we learned along the way!

## What We Learned

We learned how to incorporate already existing Machine Learning strategies and combine them to improve our collective accuracy in making predictions for stocks. We learned a ton about the different ways that one can analyze stocks, and we had a great time slotting together all of the different APIs, libraries, and other technologies that we used to make this project a reality.

## What's Next for PocketAnalyst

This isn't the last you've heard from us!

We aim to better fine-tune our stock recommendation algorithm. We believe that are other parameters that were not yet accounted for that can better improve the accuracy of our recommendations; Down the line, we hope to be able to partner with finance professionals to provide more insights that we can incorporate into the algorithm.
## Inspiration

My father put me in charge of his finances and in contact with his advisor, a young, enterprising financial consultant eager to make large returns. That might sound pretty good, but to someone financially conservative like my father doesn't really want that kind of risk in this stage of his life. The opposite happened to my brother, who has time to spare and money to lose, but had a conservative advisor that didn't have the same fire. Both stopped their advisory services, but that came with its own problems. The issue is that most advisors have a preferred field but knowledge of everything, which makes the unknowing client susceptible to settling with someone who doesn't share their goals.

## What it does

Resonance analyses personal and investment traits to make the best matches between an individual and an advisor. We use basic information any financial institution has about their clients and financial assets as well as past interactions to create a deep and objective measure of interaction quality and maximize it through optimal matches.

## How we built it

The whole program is built in python using several libraries for gathering financial data, processing and building scalable models using aws. The main differential of our model is its full utilization of past data during training to make analyses more wholistic and accurate. Instead of going with a classification solution or neural network, we combine several models to analyze specific user features and classify broad features before the main model, where we build a regression model for each category.

## Challenges we ran into

Our group member crucial to building a front-end could not make it, so our designs are not fully interactive. We also had much to code but not enough time to debug, which makes the software unable to fully work. We spent a significant amount of time to figure out a logical way to measure the quality of interaction between clients and financial consultants. We came up with our own algorithm to quantify non-numerical data, as well as rating clients' investment habits on a numerical scale. We assigned a numerical bonus to clients who consistently invest at a certain rate. The Mathematics behind Resonance was one of the biggest challenges we encountered, but it ended up being the foundation of the whole idea.

## Accomplishments that we're proud of

Learning a whole new machine learning framework using SageMaker and crafting custom, objective algorithms for measuring interaction quality and fully utilizing past interaction data during training by using an innovative approach to categorical model building.

## What we learned

Coding might not take that long, but making it fully work takes just as much time.

## What's next for Resonance

Finish building the model and possibly trying to incubate it.
## Inspiration

GET RICH QUICK!

## What it does

Do you want to get rich? Are you tired of the man holding you down? Then, WeakLink.Ai is for you! Our app comes equipped with predictive software to suggest the most beneficial stocks to buy based off your preferences. Simply said, a personal stock broker in your pocket.

## How we built it

Weaklink.Ai front end is built using the Dash framework for python. The partnered transactions are preformed with the assistance of Standard Library where our back end calculation engine uses modern machine learning techniques to make decisions about the best time to buy or sell a specific stock. Confirmation is sent to the user's mobile device via Twilio. Upon confirmation the workflow will execute the buy or sell transaction. The back end engine was custom built in python by one of our engineers.

## Challenges we ran into

It was difficult to scrape the web for precise data in a timely and financially efficient fashion.
It was very challenging to integrate Blockstack into a full python environment.
The front end design was reformatted several times.
There was some learning curves adjusting to never before seen or used api.
Finding financially efficient solutions to some api

## Accomplishments that we're proud of

Despite the various challenges we are proud of our project. The front was more visually appealing than anticipated. The transition from back end calculations to visual inspection was relatively seamless. This was our first time working with each other and we had very good synergy, we were able to divide up the work and support one another along the way each taking part in touching each aspect of the project.

## What we learned

The various api available as well as some of their limitations. We discovered that open source api is often more helpful than a closed source black box. We also learned a lot about data security via Blockstack. Lastly we learned about various ways to interpret and analyze stocks in a quantitative fashion.

## What's next for WeakLink.Ai

There is a lot of work left for us. The most immediate priority would be to set up trend analysis based on historical data of the user followed by more customization options. A place for the user to describe their desires and our machine learning algorithm will take that information into account in order to recommend actions of the user which is in their best interest.