---
# System prepended metadata

title: Monēta Litepaper

---

# Monēta Litepaper 

<table style="width: 100%">
    <tbody style="width: 100%; display: inline-table">
      <tr>
        <th>Authors</th>
        <td>Nikola, Ivan</td>
      </tr>
      <tr>
        <th>Status</th>
        <td>Draft</td>
      </tr>
      <tr>
        <th>Version</th>
        <td>0.3</td>
      </tr>
  </tbody>
</table>

## Abstract

Monēta is a line of products that aim to bring [trustless](#Glossary) algorithmic trading to the general market. Open-source and community-driven, targeting the [_algo-creators_](#Glossary) and [_algo-traders_](#Glossary) needs and wants. Our ambition is to bring market fairness and equality of opportunity to traders and creators by democratizing trade algorithms development and consumption.

## Motivation
The world is moving towards automation. Financial institutions with enormous capital reserves have benefited from automated strategies for years. The situation for traders that are outside of these financial entities is drastically different. Taking up investing through algorithmic trading without intermediaries is pretty much unexplored.

Overall two main challenges need to be overcome to create a healthy market for Algo creators and Algo traders to operate in.

1. Algo-creators need the proper incentive and tooling to produce algorithms speedily and reliably. Capable developers and traditional traders can find the main source of income in automated strategies.
2. Both creators and traders need an algorithm's performance to be accurately represented price-wise. That price needs to reflect the secondary market's sentiment as well.

### Competitive environment 
Non-algo traders will struggle competing with traders that employ algoritmic strategies. According to statistics:
   - Algorithmic trading contributed nearly **60-73**% of all U.S. equity trading in 2018
   - Around **92**% of trading in the Forex market was performed by trading algorithms instead of humans

There is an obvious upward trend in algorithmic trading adoption in both enterprise and standalone users. Being ahead of the curve is going to prove critical in the coming years.

### Time-to-market
[_Algo-creators_](#Glossary) are faced with multiple challenges while developing a profitable algorithm:
- **Effort** - trading applications share a huge amount of common code - everything except the algorithm itself. Writing a continuously profitable algorithm is challenging enough by itself. Spending valuable time on repetitive tasks building an entire trading application is not productive.
- **Analysis** - the ability to understand a strategy's impact on a portfolio is a necessity.

Solving those challenges will not only have an impact over the time-to-market but will also increase the likelihood of [_algo-creators_](#Glossary) producing more with no compromise in quality.

### Trust
Most platforms for creators store the produced algorithms on their side - what happens with that data is something [_algo-creators_](#Glossary) don’t have any control over. Most [_algo-creators_](#Glossary) are fully capable of running the algorithms on their side with no centralized parties involved.

### Monetization
The ability to capitalize on an algorithm without exposing its intricacies is a must-have. [_Algo-creators_](#Glossary) can/should be able to grant further access to the decisions taken by the algorithm to [_algo-traders_](#Glossary) in order to profit an additional fee. To make such an algorithm appealing to [_algo-traders_](#Glossary) there needs to be a decentralized way for them to mirror said algorithm’s decision as well as proof of its performance.

## What can we do about it?
The first step of making algo-trading more open to the public is setting up a creator environment. We must ensure that [_algo-creators_](#Glossary) have all the necessary tools to produce a profitable algorithm with high confidence.

### Studio
Monēta Studio provides the tools needed by an [_algo-creator_](#Glossary). Starting with exploration the creator is guided through a process that completes with their automated strategy ready to be executed against exchange of their choice*.

- **Simplicity** - two distinct trading apps share a large amount of boilerplate logic. Monēta studio takes care of that and lets the [_algo-creator_](#Glossary) focus on the most crucial part of their trading app - the algorithm.
- **Trustless** - The entire process of writing, back-testing, and compilation is done entirely on the clients' side.
- **Analysis** - Performing accurate back-testing on a granular scope of historical data and the ability to evaluate the results are a must if the trading app is to be robust and with **predictable ROI**.
- **Machine Learning** - data produced from back-testing sessions can be cherry-picked (e.g. selecting the most profitable trades from a cross of multiple sessions) providing a labeled dataset that is suitable for neural network training. With this writing, the algorithmic code becomes an intermediate step in the development of a more profitable ML enhanced algorithm.

The trading application produced by **Monēta Studio** can perform in a standalone fashion, but it also has built-in integration with the Monēta protocol.

\* *Exchange that is supported by **Monēta Studio***.

### Protocol
A decentralized algorithmic trading protocol, that allows consumers to copy-trade algorithms. 

- **Transparency** - to build confidence in _algo-traders_ an algorithm provides meaningful information which is based on its historical performance. Every live trade can be inspected.
- **Monetization** - _algo-creators_ need to be incentivized for their contribution. A % based fee is taken from each profitable transaction - part for the creator and part for the protocol treasury.

<iframe width="768" height="432" src="https://miro.com/app/embed/o9J_lB3iZNQ=/?pres=1&frameId=3074457366473294264" frameBorder="0" scrolling="no" allowFullScreen></iframe>

### Integrating with Synthetix
The impact algorithmic trading can have on an exchange is often unpredictable. Automated strategies are blamed for flash crashes and can cause market volatility and unpredictability.

- **Operating with derivatives** - Synthetix allows us to be predictable in our trades while having no impact on the actual market.
- **Incentive program** - While the volume might increase greatly for the Synthetix protocol, every win that trading applications make will take a toll on Synthetix stakers. Monēta's [monetization strategy](https://hackmd.io/@KKPku2MmR-esxIFlf9NHRw/HJ_lb-k4Y?#Fees) provides a solution.

## How it started?
Two engineers with hands-on experience in investment and interest in DeFi - Ivan Prodanov and Nikola Velichkov, share the same vision about technology and the positive impact it could have. Over the last 10+ years, they successfully collaborated on multiple projects. They saw the potential in algorithmic trading. Through experience, they were able to recognize problems in the space and propose solutions to them. Over the years of development, the idea evolved from a passion project into a trustless platform for algorithmic trading - Monēta.

```mermaid
gantt
    title A brief history
    dateFormat  YYYY-MM-DD
    axisFormat  %Y-%m

    section Phase 1
    1. POC           :done, 2019-10-01, 2020-05-01
    2. First demo    :critical, 2020-05-01, 2d
    3. In-app code editor     :done, 2020-05-01, 2020-06-01
    section Phase 2
    4. WebAssembly SDK      :done, 2020-06-01, 2021-03-01
    5. Data Model     :done, 2020-06-01, 2020-10-01
    6. WebApp UX      :done, 2020-07-01, 2021-05-01
    section Phase 3
    7. Full Editor features :2021-03-01, 2021-10-15
    8. Trustless WebApp :2021-06-01, 2022-03-01
    
```

### Phase 1 - POC
1. POC - consisted of a headless application that performed calculations and analysis and a web application that visualized the results.
2. First demo - Implemented an algorithm owned by an *algo-creator* and managed to improve it based on the analysis produced during back-testing.
3. In-app code editor - revamped UI and added a code editor. Users can now write algorithms by themselves.
### Phase 2 - Trustless POC
4. WebAssembly SDK - developed a multi-purpose Wasm.SDK that enabled the transition to a trustless Web application.
5. Data Model - invested time in organizing the data model to allow future ML integration.
6. WebApp UX - major improvements over the UI and general user experience. Simplified and organized workflows.
### Phase 3 - MVP
7. Full Editor Features - Adding the ability to support multiple files, in-browser compilation, and the ability to export executables.
8. Trustless WebApp - transition backtesting and data storage to the web app.

## Glossary

###### Trustless: The goal of trustless applications is to minimize the amount of trust a user has to put in. There are different ways to go about this - full transparency, eliminating intermediaries, etc.
###### algo-creators: The people responsible for algorithm creation and maintenance.
###### algo-traders: The people that mirror-trade automated trade algorithms. In the context of Monēta, all algo-traders are algo-token holders.