The Eve of new

How Eve works

Some Facts



Annual Brokerage Fines in Millions


Percent of the country invested in the market


Avg. Full Service Brokerage Fee


Percent of the country that knows computer science

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.


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.

Steps to make Eve Scalable

Step 1: Build a novel product
Step 2: Attract users
Step 3: Develop a community resource forum
Step 4: Reimforcement ML improves the product
Step 5: Domino effects grow impact exponenitally.


The user verbally outlines an algorithmic trading strategy to Eve.


Eve recognizes the voice command and converts it to text.


Eve performs noise removal and lexical normalization.


The text is syntactically analyzed by Eve using our proprietary context-free grammar and converted into a parse tree.


The text is translated from the Eve language into Python code via the parse tree.


The script is executed and automatically trades according the user’s specifications.


An inside look at the financial computation and executation tasks that Eve is capable of performing.

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

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