Algotrading competition on gTrade by TechnoTrading AI

Proposals
1

Rationale & Motivation

The share of trading volume executed by algorithmic and AI-driven systems is expected to grow significantly as technological capabilities continue to advance. Algorithmic trading is a rapidly expanding market, and when robust and sustainable strategies are developed, algorithmic systems have the potential to outperform human traders.

Many traditional Web2 financial institutions already manage portfolios primarily through algorithmic systems. This presents a clear opportunity for Web3 protocols to adopt similar approaches. Trading is a global and highly competitive market, and marketplaces, asset managers, and strategists are likely to achieve higher profitability by investing in algorithmic trading capabilities.

What Is Technotrading & rethink.finance and the Impact on gTrade

Technotrading is a Web3 company specialized in executing algorithmic trading strategies on gTrade. The company owns the intellectual property for an adapter capable of executing a large number of trades and handling high trading volumes in an automated manner.

Technotrading is integrated with rethink.finance, a protocol that secures and powers trading vaults and enables capital pooling. Through this integration, Technotrading has already operated an algorithmic trading vault on gTrade via rethink.finance.

Together, these two protocols provide a unique value proposition for gTrade. Technotrading is able to execute algorithmic trading strategies on pooled capital managed through rethink.finance. On other vault providers, such as Enzyme, successful yield strategies have historically attracted vault sizes in the double-digit millions.

The linked Excel file contains a volume simulation illustrating the potential trading volume that could be generated on gTrade through algorithmically managed vaults.

The parameters are defined as follows:

  1. Number of algo trading vaults sourced through competition refers to the number of successful strategies expected to be identified.

  2. Average leverage refers to the leverage utilized by the vaults.

  3. Average trading vault size refers to the amount of capital pooled into each vault immediately after launch.

  4. Average monthly vault growth refers to the expected monthly increase in vault size through joint business development efforts.

  5. Average number of trades per vault refers to the expected number of trades each strategy executes per day.

  6. Average vault growth per month refers to the expected monthly PnL generated by the algorithms.

  7. Fees refer to the applicable trading and protocol fees.

  8. Opening and closing factor refers to the combined opening and closing trading volume.

  9. Average days per month is used to extrapolate volume on a monthly basis.

Note: The infrastructure supports more than 1,000 trades per day, assuming strategies perform as intended. The scalability potential and corresponding impact on gTrade are therefore substantial.

In summary, the logic behind the proposal is to:

  • Identify profitable algorithmic trading strategies through a competition

  • Establish vaults to pool capital into these strategies

  • Scale vault participation and, consequently, increase trading volume on gTrade

Algorithmic Trading Competition

Technotrading’s primary challenge at present is identifying profitable trading strategies, largely due to funding constraints. To address this, the proposal is to jointly host an algorithmic trading competition with gTrade, thereby expanding Technotrading’s strategy pipeline while increasing long-term trading volume on gTrade.

The objective of the competition is to incentivize data scientists, traders, and algorithmic traders to collaborate with Technotrading and gTrade in researching and developing algorithmic trading strategies. The total reward pool will be 100k. Technotrading, in collaboration with gTrade, will curate 3–5 teams from their respective developer communities on Meetup (PyMunich and AI Munich). Each selected team will receive 5k to cover data access, GPU usage, and other essential research resources.

Once teams are selected, a three-month strategy development phase will begin, during which participants will research and build algorithmic trading strategies. This will be followed by a joint launch and a three-month live strategy assessment phase, during which all strategies will be battle-tested in a real trading environment.

Profitable strategies will then be deployed as trading vaults on rethink.finance, allowing the gTrade community to allocate capital to the most successful strategies.

Post-Competition Plans & Benefits for gTrade

The primary objective of the competition is to drive sustained long-term trading volume on gTrade and to onboard new users through rethink.finance vaults. Technotrading guarantees lifetime exclusivity of the strategies sourced through the competition for gTrade, provided that key conditions—such as fees and trade execution—remain mutually beneficial for all parties.

Beyond long-term volume growth, gTrade also benefits from increased marketing exposure and brand positioning as a protocol actively investing in algorithmic trading innovation.

In addition, the competition reward funds will be locked into the vaults for a period of one year, ensuring that the allocated budget remains active and generates trading volume within the ecosystem.

Community Approach and Marketing Plan

The two founders of Technotrading, Johannes Sommer and Anton Caceres, manage large-scale developer communities on Meetup. One community focuses on Python, the primary programming language used in data science and quantitative research, while the other is centered on artificial intelligence. Combined, these communities reach approximately 8,000 developers and experts across Germany.

In addition, a targeted marketing campaign will be conducted across various Discord communities of paid trading signal providers available on TradingView. This outreach aims to attract algorithmic traders who build strategies based on these indicators. Targeted providers include LuxAlgo, IA, and ggshot.

Ask

  • 100k in reward funding for the trading competition

  • 5k in data and research funding per team

  • Start date: April 1, 2026

  • Trading start date: July 1, 2026

Goal: Increase long-term trading volume on gTrade by 2×

2

I’m very supportive of this direction. Bringing a structured pipeline for algorithmic strategies into gTrade feels like a natural evolution, and the approach of sourcing external talent is a smart way to expand what can be built here. This has the potential to become a strong, repeatable driver of activity and positioning for the protocol.

As I was reading through, a few points came to mind that I’d be interested to understand in a bit more detail:

Strategy Selection & What Qualifies as “Profitable”

When it comes to strategies being deployed into vaults, how is “profitable” being defined in practice?

  • Are there specific thresholds around drawdown or risk-adjusted returns?

  • Is there a focus on consistency over time rather than short-term performance?

Feels like this will play a big role in shaping the overall quality of what gets deployed.

Live Testing & Capital Allocation

The 3-month live phase makes sense structurally, and I’m curious how capital is handled within that process.

  • Are strategies initially deployed with smaller allocations and then scaled?

  • Or do they move into larger vaults after the testing phase?

Mainly interested in how exposure is managed as strategies prove themselves.

Vault-Level Risk Controls

Given how important trust is around vaults, it would be helpful to understand what safeguards are planned.

  • Are there predefined drawdown limits or pause mechanisms?

  • Any form of exposure caps or circuit breakers?

Making this explicit could really strengthen confidence from a user perspective.

Transparency Around Performance

From a community perspective, how is performance expected to be shared?

  • Will there be a public view of metrics like PnL, drawdown, win rate, etc.?

  • And a clear separation between backtested and live results?

That level of visibility would make it much easier to evaluate and participate.

Incentives Beyond the Competition

On the incentive side, I’m wondering how strategy creators are aligned over the longer term.

  • Is there any participation in ongoing vault performance or fees?

  • Or is it mainly tied to the competition and initial funding?

Feels like this could be an important lever for encouraging durable strategies.

Role of Technotrading

Since a lot of this flows through Technotrading, it would be interesting to understand how that relationship is structured longer term.

  • What sits with gTrade vs what’s handled externally?

  • Is there any plan to internalise parts of this over time, or is it intended to remain external?

Defining Success

The goal of increasing volume is clear, and I’m curious how success is being viewed alongside that.

  • Is there a focus on sustainability of that volume over time?

  • Or metrics like vault profitability and user retention?

Just thinking about how to capture the longer-term impact.

Volume Simulation

The volume simulation is a great addition and gives a clear view of the potential upside as strategies scale.

I’d be interested to understand how this looks under more conservative assumptions as well, for example:

  • if only a subset of strategies remain profitable over time

  • or if performance evolves as capital scales

Seeing how sensitive the model is to those variables could help frame both the base case and the upside.

Closing

Overall, I think this is a strong proposal and a very positive direction for gTrade. The questions above are mainly around how some of the key pieces are defined and implemented in practice. With a bit more clarity there, this feels like something that could develop into a really solid foundation for the ecosystem.

3

Hello sir! thanks for your support and interest in the proposal, let me try to clarify as many questions as possible, answered in bold (sorry for messed up formatting but this editor and the answer format makes it hard haha)

Strategy selection:

Are there specific thresholds around drawdown or risk-adjusted returns? –> Yes we qualify teams with good track records and well adjusted R:R ratios and drawdowns

Is there a focus on consistency over time rather than short-term performance? –> **Yes most teams that we would get into the competition have a track record of running algos and hence could considered as consistent. Teams that dont have a track record have the chance to prove themselves over the 3 month trial period

Testing
**
Are strategies initially deployed with smaller allocations and then scaled? –> Yes, so obviously we will try to push the vaults as much as we can, especially the ones with track record to already give good allocation in the test period but we dont want to hurry, but rather make it slow and good

Or do they move into larger vaults after the testing phase? –> after the testing phase, we push hard on BD together with the different teams and hopefully as a community attract large capital to the vaults

Vault-Level Risk Controls

Are there predefined drawdown limits or pause mechanisms? –> we can define these together, typically bots can have drawdowns of around 20% but then again its on the team to facilitate risk management. However longterm, we could introduce a vault of vault concept of rethink side. That would mean, all the vaults would be channeled into one big vault to increase institutional credibility. The vaults would be subject to a risk management framework that manages risks and exposure of the bigger vault - this would be a cool end game scenario.

Any form of exposure caps or circuit breakers? –> also on the teams to manage, but typically teams would have 10+ trading pairs and managing exposure of the different positions to these.

Transparency Around Performance

Will there be a public view of metrics like PnL, drawdown, win rate, etc.? –> sure we can do that.

And a clear separation between backtested and live results? –> yeah all that falls into the competition is live trading anyway so backtesting wouldnt be allowed in the competition.

Incentives Beyond the Competition

Is there any participation in ongoing vault performance or fees? –> well gTrade would get the trading fees, the 100k reward money is locked into the vault. No participation in performance fee as this is on the teams + technotrading to align on

Or is it mainly tied to the competition and initial funding? –> for gTrade the play is institutional trading volume being onboarded through the vaults

Role of Technotrading

What sits with gTrade vs what’s handled externally? –> gTrade executes the trades, technotrading feeds them through the SDK / backend of gTrade

Is there any plan to internalise parts of this over time, or is it intended to remain external? –> Technotrading IP and value proposition will remain TechnoTrading IP and value prop but of course we are all aligned on longterm success such as a) increasing vault sizes b) minimizing risks c) increasing trading volume d) ensuring longterm sustainability of strats.

Defining Success

Is there a focus on sustainability of that volume over time? –> yes sustainablity of volume is one key success metric and we will align on KPIs jointly between gTrade and Technotrading

Or metrics like vault profitability and user retention? –> profitability is the second key metric because the best trading volume does nothing good if you quickly nuke your funds

Volume Simulation

if only a subset of strategies remain profitable over time –> you should be able to play around with it? Otherwise I will give you editor access

or if performance evolves as capital scales –> yes so as we are targeting highly liquid pairs we dont expect spreads for example to eat up profits soon, but this is a good point.

Hope that helps and if you have further questions we can also hop on a quick chat!

Cheers from Munich!

Johannes