Monark

One engine. AI side, DeFi side.

$MONARK
CA: FYZcYCHSp8FzNba1UtDZydKKGosmxVNpFBiVuia38AhT

[Monark @usemonark]

https://t.co/pOfvT7NgTA

[Monark @usemonark]

@JMironti77294 Today is expected to mark the final red daily candle on Bitcoin and the beginning of a prolonged sequence of green candles.

[Monark @usemonark]

Who has been here since the very first week? 🤖

[Monark @usemonark]

The first billions have been reached in the volume of tokens processed by our agents. In a single week, 3 billion tokens were handled across all models combined.

To pursue the next billion-scale milestone within the broader Monark project, a Monark agent will be allocated a portfolio of between 1,000 and 3,000 dollars. It will be given one clear objective: grow that capital to 1 million dollars, across all markets.

🔖 This tweet.

[Monark @usemonark]

@Waseems36486472 @v1galaxy1

[Monark @usemonark]

The strategy screener analyzes market data every two minutes. It recalculates the necessary metrics and transmits the results to the agent. Once decisions are taken, the agent can automatically generate alerts according to the configuration of each asset in order to reinforce exposure.

Every decision is accompanied by a clear plan and forward looking calculations that define when to add to a position, when to take profits, how many reinforcements remain possible, and the profit taking logic for each reinforcement. This process runs continuously, every two minutes.

The screener currently ranks the top fifteen assets by score. You may leave the ranking unchanged or select which cryptocurrencies to prioritize. This is the base version, not the final one.

The screener serves several important purposes. It reduces inference costs, helps prevent hallucinations, and avoids overloading the gate. These are only a few of the advantages. Many more will be shared with you, and others you will discover yourself.

[Monark @usemonark]

Most participants have chosen to focus on spot and perpetual markets. Investing in cryptocurrencies is a structured process that requires both a clear strategy and a defined methodology.

Aggregators such as CoinGecko make it possible to analyze performance by sector or by ecosystem. In every bull cycle, specific sectors take the lead and outperform the broader market. Data allows us to identify these rotations early, though fundamental analysis and narrative evaluation remain essential.

Throughout this process, the human remains in the loop. If the agent identifies a sector with higher potential and proposes concentrating capital there, you decide whether to approve it and how much capital to allocate. This will form the first layer of fundamental and on-chain analysis.

We are still designing this component. Once completed, it will strengthen the technical analysis performed after the gate’s decisions. By the time news becomes public, the underlying trend has usually already begun.

A current example illustrates this approach. Sentiment has recently turned bearish, yet the strategy remains bullish and continues to accumulate through dollar cost averaging. We do not wait for sentiment to turn positive. We build positions in anticipation and reinforce them on confirmation. Spot exposure is prioritized, with perpetual positions used to support it.

Everything ultimately passes through a conformal gate. We abstain when caution is required and act only when conditions justify it.

[Monark @usemonark]

Perps OR Spot?

[Monark @usemonark]

What challenge are we going to give our agent?

Real money. Real markets.

Drop your most WTF ideas, the crazier the better. We’re going all out. 🔥

Reply with yours 👇

[Monark @usemonark]

Perps OR Spot?

[Monark @usemonark]

If someone offers you a strategy or an agent that claims to deliver consistent returns over the very long term, credibility requires full transparency. There must be a public portfolio, publicly verified testing, and publicly documented performance. Everything has to be independently verifiable. That is the real challenge.

Without this level of openness, such claims cannot be taken seriously.

Things are about to change.

[Monark @usemonark]

Over a six-day period, fifteen different models processed 2.5 billion tokens across more than 300 financial instruments spanning every major market: cryptocurrencies, indices, forex, and commodities.

Calibration followed calibration, supported by continuous audits. Every decision made by the gate was examined in detail, and each model contributed its own conclusions, ranging from the most optimistic to the most cautious.

Millions of candles and historical price charts were analyzed, some extending back a century. The approach remains pure technical analysis. No news, no external inputs, only the study of price action itself.

Monark never sleeps.

[Monark @usemonark]

To assess the potential value an AI agent could bring to the Monark engine and its embedded strategy, we conducted extensive testing across multiple models. The results achieved with Anthropic models were particularly impressive.

A key advantage of this approach is that the Monark engine and its screener will perform a substantial share of the workload, significantly reducing inference costs.

We invite qualified individuals to contact us starting today to express interest in joining the beta tests. We will not initiate contact via direct message. Participants must possess a solid level of technical knowledge, and only approximately twenty places are available.

In addition to the agents that will operate in streaming mode, we are seeking meaningful feedback from the community. We also hope that selected partners will take part in the testing phase.

Significant developments are underway. A major transition is taking place as we move from the alpha phase into beta. This marks a fundamental step forward for the project.

[Monark @usemonark]

We already account for the fact that the strongest buying opportunities on the market usually come with stop hunts and exaggerated moves. These events happen under recognizable conditions, so both the engine and the agent are aware of them. Instead of entering with a single order, the agent dollar-cost averages into the zone and splits the position into small lots sized according to the timeframe and the target horizon. The objective is simply to get the best possible average entry price.

This mechanism will be significantly improved once we add on-chain intelligence. That will include a library of on-chain patterns, analysis of how wealth is distributed, identification of who is selling to trigger stops, mapping of liquidation zones, detection of when a liquidation wall is about to break, and recognition of when a liquidation cascade starts reversing.

Independently of market noise and the hysterical psychology that paralyzes the masses, Monark keeps buying the market under a conformal gate. still Bullish.

[Monark @usemonark]

The Martingale strategy has filtered a shortlist from the top 100: BTC, ETH, BNB, SOL, NEAR, AAVE, DASH and ETHFI. No fundamentals, no on-chain metrics and no news were used. Selection is driven solely by the Martingale screener, then ranked by the number of independent confirmations. Higher-ranked names receive a larger share of capital.

No position is taken in a single order. Each allocation is split into four tranches, each governed by its own validation and invalidation rules. The conformal gate reviews every step and decides whether to proceed, wait or exit. On ETH the gate declined even the first tranche.

These charts show the first live interactions and the initial entries. In one sentence: MONARK is bullish.

[Monark @usemonark]

Did you know that as of today there is still no consistently profitable autonomous AI trading agent on a risk adjusted basis?

In open contests with real capital the best human traders outperform the top LLM based agents by 5x or more on Sharpe and P&L. Yield optimization is different. Once the problem is open (direction, timing, sizing, regime) the agents get crushed by variance, fees and hallucinations.

That is why we built a completely different architecture from scratch.

We use conformal inference to produce statistically valid prediction intervals. A separate formally verified component only accepts the positive expected value an AI model can contribute. It rejects everything that carries model risk, uncertainty or unquantified downside.

The entire system runs in a Lean 4 environment with machine checked proofs of safety properties.

No free form agent that decides.
Only bounded, proven alpha extraction.

[Monark @usemonark]

@v1galaxy1 @Maynard100x DM

[Monark @usemonark]

UPDATE 🦋

In two weeks, we are launching our beta version featuring an autonomous multi-timeframe adaptive strategy engine that operates completely independently without requiring an AI agent to execute trades. The Conformal Gate serves as a dual infrastructure for both the standalone strategy engine and any eventual AI agent connected to it. While the underlying strategy is already proven, executing a trading plan requires dynamic patience, a trait automated systems and AI agents naturally lack. Adaptive conformal inference addresses this by continually optimizing execution, ensuring the core engine runs autonomously while the optional AI layer acts as a value multiplier to boost performance, improve yields, and reduce drawdowns.

Value generation and solution usage are driven by execution rights that deplete over time based on system activity across both errors and profitable positions. Access can be purchased using either USDC or MONARK tokens. To streamline onboarding, payments made in USDC will automatically have a portion converted into MONARK. Qualified MONARK token holders earn passive usage rights via accumulated holding credits, which can be redeemed to deploy an AI agent or to directly copy the engine’s real-time execution decisions.

The architecture also incorporates GENKAN, a lean security kernel operating on least-privilege principles that provides a formally secured environment with strict isolation of API secrets and access rights, featuring customizable parameters with optimized defaults tailored for trading. Ultimately, the product caters to both AI enthusiasts and traders who prefer pure algorithmic execution without AI dependency, using AI strictly for deeper analytics and revenue optimization.

Additionally, one or several independent, autonomous agents will be streamed live, initially trading on testnet for a minimum of one month to fine-tune screening and parameters. Broadcasting their decision-making process in real time will transparently showcase and validate the real-world value added by adaptive conformal inference

[Monark @usemonark]

@Maynard100x Wanna join?

[Monark @usemonark]

We’ve tested every agent available on the market, and the one that best matches what we need is the Hermes agent from @NousResearch

As we explained before, the Monark engine is deliberately designed to run without AI. That anti-fragile approach is intentional. Any AI or agent we add has to deliver genuine added value, otherwise there’s no point including it.

The real value Hermes brings is evolutivity. Our strategy is already adaptive and evolving, but it has clear limits. It can’t recognize patterns it has never seen, and we can’t talk to it or adjust it in real time. The agent solves both problems. It also gives us a clean place to stack additional capabilities, notably fundamental analysis, which becomes necessary once we move into swing trading on traditional markets (forex, indices, and commodities).

The reason we’re expanding beyond crypto is straightforward. Crypto tends to be binary. When it’s trending hard it works well, but when it ranges or stalls, small errors compound quickly and losses accumulate. Meanwhile, real directional moves are often happening elsewhere. Other markets frequently reach major pivots and start trending while crypto is going nowhere. By covering those markets we stay in the game instead of sitting idle during crypto’s quiet periods.

After testing roughly a dozen agents, each with its own constraints, Hermes stands out as the most evolutive, future-proof, and easily upgradable option. That’s why it’s the one we’re integrating.

[Monark @usemonark]

@JMironti77294 Shall we make a deal? What our agents will do, according to our recommended parameters VS what any other strategy or any other agent will do. First condition: no intervention in a bull or bear market

[Monark @usemonark]

LOL 😅, no, This is the reply from someone with a lot of hard won experience, many sleepless nights spent in front of old screens. We’re confident that what we’re building is going to make a real splash. At the same time, we deliberately won’t share our strategy so we don’t get copied. We accept that some people will reverse engineer parts of it, and we fully stand by that choice to keep it secret.

[Monark @usemonark]

It is calibrated to detect reversals through divergences in relative strength that signal a move is reaching exhaustion, and it is calibrated to act accordingly. Every exhaustion produces momentum signals that propagate from shorter timeframes to longer timeframes, while divergences originate from the longer timeframes. It interprets this relationship and generates money management conditions that govern the entire system. The basic functioning is conditioned by polarities. This is what was meant when it was said that the agent must position itself on a chart.