Replicats is pioneering a no-code autonomous trading Agent platform on which anyone can build their own Trading Agent or copy-trade existing ones, like Replicat-One. We are an intelligence layer that empowers investors with advanced portfolio and risk management and insights through our Trading Agents, which are built on our Replicats Intelligence Framework ("RIF").
At Replicats, we believe that making a living in crypto can be enjoyable yet quite complex, with several pitfalls. In this article, we introduce our first Agent, Replicat-One, which can serve as a state-of-the-art portfolio manager and provide users with peace of mind through its autonomous trading capabilities.
Replicat-One (Agent) is the first Trading Agent of Replicats (platform), built using the Replicats Intelligence Framework ("RIF").
Users can copy-trade our Agent as soon as our platform goes live. (ETA: Q2 2025)
$RCAT is a token for the Replicat-One Agent. Initially, users will need it to see the Replicat-One internal dashboard.
Other mechanics may be added to the $RCAT token in the future.
Our platform will let users build their own Trading Agents using RIF. (ETA: Q2 2025)
We're the Replicats team, and we're here to change the way people invest.
Using Replicat-One will require no coding knowledge, making sophisticated portfolio management accessible to everyone. Users will fund their Agent's wallet, and the Agent will handle all trading operations completely autonomously. The experience combines several key benefits:
Set and Forget: Once deployed, Trading Agents will operate 24/7, constantly analyzing market conditions and executing trades with precision.
Emotion-Free Trading: By removing emotional decision-making from the equation, agents avoid common human pitfalls like FOMO buying and panic selling.
Risk-Controlled Exposure: The sophisticated risk management framework helps users gain exposure to high-growth opportunities while controlling risk using various professional approaches. In this market, keeping profits and avoiding roundtrips are essential.
Real-Time Visibility: Though fully autonomous, users maintain complete visibility into the Agent's activities and performance through an intuitive dashboard. To be released Q2 2025.
Institutional-Grade Technology: The same advanced algorithms, models, and frameworks that professional trading firms use will be accessible to individual investors—your own Quant and Portfolio manager team in your pocket.
The recent surge of interest in AI will drive innovation and attention in the next quarters. DeFi platforms like Aerodrome, Morpho, and Pendle are consolidating as top players in the Base ecosystem, and Memecoins and RWAs still have their moments in the sun. Meanwhile, Bitcoin is still the dominant crypto asset, driving the bull run and generating solid returns.
It's challenging for the average investor to decide when and how to rebalance between all these different assets, navigating narratives and trends that change every other day.
Too much time is spent on reactive trading, from chart-watching to endlessly scanning social media. Retail investors can't (and probably shouldn't) spend their entire lives glued to screens.
Replicat-One uses our proprietary AI framework to maximize exposure to these assets and navigate these trends, managing a portfolio quantitatively and automated, 24/7, fully onchain.
Replicat-One manages a long-only portfolio of BTC (cbBTC), ETH, DeFi, AI, and Memecoins on Base. It aims for returns exceeding BTC alone while controlling risk through different methods. The strategy actively reallocates among established and new AI tokens, ensuring smart exposure to DeFi and Memecoins while tracking Bitcoin's overall trend.
The proposed Agent combines an adaptive crypto market-tuned factor investing framework with equally adaptive, efficient frontier practices to find optimal asset allocation.
Given crypto's volatility, factor selection and balancing optimize risk-adjusted returns (Sharpe/Sortino). Key factors include high training return momentum, asset class signaling (high NVT, small caps, DeFi use cases), ETF movements, cointegration with traditional assets (FX, indexes), market microstructure (depth, concentration, volume distribution), network value, crypto market seasonality, intangible valuation (IHLM), factor timing, and decay.
A variant of the Black-Litterman framework incorporates insights from different sources into a Bayesian framework. This includes variance-adjusted technical indicators and deep learning GNN forecasting with multidimensional model reinforcement learning.
In a stressed full valuation, the framework also monitors factor exposures as triggers for threshold-based rebalancing within the proposed adaptive minimum-variance portfolio (AMVP), accounting for nonlinear dependencies.
These methods collectively improve state-of-the-art quantitative adaptive risk management and portfolio diversification, enabling the construction of alternative capital market lines (and even tradeable risk metrics). Findings further emphasize the adaptability of this Agent framework in identifying and responding to structural market breaks, identified significant market events, and shifts in volatility regimes.
Framework
The proposed framework for Replicat-One combines an adaptive factor investing framework tuned for crypto markets with well-established efficient frontier portfolio management for optimal asset allocation.
Factor selection and book rebalancing optimize risk-adjusted return metrics (sharpe/sortino). Selected features - by variance explainability - include high training return momentum, crypto asset classes specificities (NVT, small caps, DeFi use cases), ETF movements, cointegration with traditional assets (FX, indexes), crypto market microstructure (depth, concentration, volume distribution), network value, crypto market seasonality, intangible valuation (IHLM), factor timing, and decay.
A variant of the Black-Litterman framework incorporates insights from different sources into a Bayesian framework. This includes variance-adjusted technical indicators and deep learning GNN forecasting with multidimensional model reinforcement learning. In a stressed full valuation, the framework also monitors factor exposures as triggers for threshold-based book rebalancing within the proposed adaptive minimum-variance portfolio (AMVP), accounting for nonlinear dependencies.
These methods collectively enhance state-of-the-art quantitative risk management and adaptive portfolio diversification. The findings further emphasize the adaptability of this Agent framework in identifying and responding to structural market breaks, which are identified as significant market events and shifts in volatility regimes.
We plan to add new tokens deployed on Base that have a lifespan of over 10 days, a market cap of $1 million, and sufficient upside potential. They will undergo initial manual vetting before we implement a proper filtering framework.
The future of trading belongs to those who can harness advanced technology and strategic insight. Replicat-One represents a new paradigm in automated portfolio management, combining sophisticated AI with the accessibility needed for widespread adoption.
To start with Replicat-One, visit replicats.ai and sign up for our launch.
Website: www.Replicats.ai
Documents: https://docs.replicats.ai/
Telegram: https://t.me/replicatsai
Paragraph: https://paragraph.xyz/@replicatsai
Disclaimer:
Please note that Replicat-One and Replicats are currently in developmental stage. Features are being deployed on an experimental basis to encourage community engagement and gather valuable feedback from our users. We want to emphasize that several platform aspects, including technical features, the business model, terms and conditions, and other relevant items, are subject to updates and additions. These updates will occur over the coming days and weeks as we work to refine and enhance the platform.
Replicat-ONE
