The Eve of new
The Problem
Quant trading is hard
Say hello to Eve
The next era of investing is here. Eve's innovative natural language processing software makes quant trading as easy as talking to your neighbor.
{This is
Disruptive}
The goal of Eve is to push to frontier of computer programming, financial trading, and social outreach. We are not content to simply sell a product, Eve is here to transform the industry.
Some Facts
173
Annual Brokerage Fines in Millions
54
Percent of the country invested in the market
120
Avg. Full Service Brokerage Fee
2
Percent of the country that knows computer science
What's so special?
There are two highly lucrative aspects to our product. The first is the market relevance. Financial trading is a multi-billion dollar industry in which the status quo of trading has remain unchallenged. Eve seeks to overthrow the convention.
The second aspect is the social implication. Eve is a catalyst for combatting wealth inequality. By cutting our the corporate middleman, Eve seeks to put an industry formerly dependent on financial elites in the hands of the general population..
Social Impact
Eve empowers the 99%. This forms the backbone of our product mission. By introducing NLP to the investment sector, we have opened new potential with our product. Machine learning, through our product, becomes an enabler for the 99%. Not only is Eve financially lucrative, it is lucrative in an area of positive social benefit. We aspire to solve the global distribution of wealth. We pinpointed a single area that produces compounding spillover effects. Global wealth inequality | Corporate elitism | Economic Stagnation - Eve is part of the solution.
Creativity
Current institutional investing is plagued with several problems. High management fees, liquidity of funds, and low returns may the system effective and the fact that 97% of the American public isn’t allowed to participate. Eve solves this through simplicity and accessibility.
Technical Complexity
Eve presents a new frontier with its complex technical software that leverages voice recognition, natural language understanding, syntactical analysis, and exchange interfaces to revolutionize cryptocurrency trading accessibility among novice investors. Voice commands are processed into text and parsed into our lexicon-free language, compiled into Python code, and executed to interact with the market in real time.
The Backend Explained
Performace Examples
Each image displays the parse tree of a given command next to the corresponding input in plain English. Click on an image to see an enlarged snippet of the parse tree. Drag the enlarged image to the left to see the rest of the image or click the buttons below.
Eve API
An inside look at the financial computation and executation tasks that Eve is capable of performing.
Operations
| Function | Argument | Parameter | Description |
|---|
| compute_RSI() | data, day | RSI_period | Exponentially smoothed moving average of gains and losses |
| computer_STOCHRSI() | data | RSI_list, min_RSI, max_RSI | Computes stochastic RSI |
| compute_SMA() | data | short_avg, long_avg, price | Returns percent increase of SMA WRT LMA |
| compute_SMA_series() | data, series | x | Returns series of EMA for different periods in series |
| compute_ADX() | n, data | up_move, down_move | Computes adverage directional index indicator |
| high() | n, day, data | none | Computes highest closing price in previous n periods |
| K() | x, data | L5, H5, price | Computes K value for x days ago |
| data_SMMA() | n, data | none | Computes smoothed average over pas n days with specified weight |
| average() | n, data | period_data | Returns simple average of past n days |
| compute_MACD() | data | short_EMA, long_EMA | Returns percent change between short and long EMA |
Examples
compute_RSI(data, day=0): exponentially smoothed moving average of gains and losses.
compute_STOCHRSI(data): computes stochastic RSI
compute_SMA(data): returns % increase of SMA wrt LMA
compute_SMA_series(data, series=[5, 10, 15, 20, 25, 30, 35]): returns series of EMA for different periods in series
low(n, day=0, data): computes lowest closing price in previous n days