-
Atletico sink Man Utd as Viking deny Bayern in Champions League
-
Few buyers for starving cattle in El Nino parched El Salvador
-
Man United's Martinez blasts board's lack of investment
-
Bybit Expands Beyond Crypto as CEO Outlines Next Phase of Financial Platform
-
Meet A7 International, an Innovative Global Platform Designed to Reinvent the International Payments Market
-
Bayern held by Viking after 'super dumb' Olise red in Champions League
-
Shakhtar hope London move a reminder of Ukraine conflict
-
Why AI makes computer worms harder to stop
-
Tresoldi hat-trick fires Brugge past Inter Milan
-
US fines Bank of America $1.2 bn for anti-money laundering violations
-
Rams axe kicker Mevis after misses cost victory over Bills: reports
-
Bayern held by Viking after Olise sees red in Champions League
-
Yamal stays cool at boiling Galatasaray to lift Barcelona to victory
-
Atletico down Carrick's struggling Man United
-
Arsenal cruise past Lille into share of Champions League lead
-
Croatia trials full driverless robotaxi service
-
Trump ousts Greece ambassador over ethics scandal
-
US stocks dip ahead of inflation data as tech sell-off hits Asia
-
Reese Witherspoon, France's Mediawan to buy Hello Sunshine
-
No lights, no friends: Cuban youth grapple with loneliness
-
Bipartisan US senators urge Trump to scrap Russian diesel deal
-
Saudi coalition strikes Yemen oil sites after Houthi attacks
-
Ex-gang leader sentenced to minimum 40 years for Tupac murder
-
French economy minister denies country caught in debt 'snowball'
-
Cornell suspends frat parties after alleged gang rape
-
Judge extends halt to Trump's golf course tree-cutting
-
Man City deserve to keep titles, says PSG's Luis Enrique
-
Milan's Saelemaekers set for lenghty spell out after ankle surgery
-
Regulator 'will resign' if any political interference in Man City case
-
Nvidia says to invest in French medical website
-
Microsoft's Brad Smith calls for 'real and effective' AI safety standards
-
Real Madrid's Mourinho has 'trust' in referees despite complaints
-
Anthropic warns of AI 'worms' spreading across smart gadgets
-
Mendis, Shanaka star in Sri Lanka consolation T20I win
-
Big US banks report strong earnings and stay bullish on AI
-
Demonstrations backing France's protesting students gather strength
-
Iraola backs Barcola, Wirtz to come good for Liverpool
-
Trump says US Senate as hard to defend as House in midterms
-
In a first, AI helps UK lawmaker find her voice in parliament
-
Suspected Russian drones hit two ships off Bulgaria, 11 missing
-
Pope Leo in Rome hospital for lung operation
-
Tech stocks slide on AI jitters
-
EU watchdog says smart glasses must give 'clear warnings' when recording
-
Trump asks Greece ambassador to resign amid ethics scandal
-
Houthis say Saudi Arabia strikes oil site in Yemen's capital
-
New White House spokeswoman heavy on Trump praise, light on questions in debut
-
France roiled by marches in support of protesting students
-
Emery urges Villa to make most of reduced home comfort
-
Goldman Sachs profits jump as CEO says no plans to exit
-
Flavio Bolsonaro visits father under house arrest ahead of Brazil runoff
AstraBit Offers Markowitz-Based Portfolio Optimization for Algorithmic Crypto Strategy Allocation
NEW YORK CITY, NY / ACCESS Newswire / June 26, 2025 / AstraBit has integrated a portfolio optimization engine grounded in Markowitz's Modern Portfolio Theory (MPT) and Post-Modern Portfolio Theory (PMPT), enabling users to apply institutional-grade allocation models to digital asset trading strategies. This feature provides information on systematic portfolio construction, based on features that include, but are not limited to, expected return, volatility, downside deviation, CAPM, and inter-strategy correlation, helping users better understand risk and potentially achieve more efficient risk-adjusted outcomes in their digital asset investing
The integration of this framework brings quantitative asset allocation methods, long used by institutional and other sophisticated money managers, into the realm of algorithmic trading for digital assets. Through AstraBit, users can analyze their manual trading and automated algorithmic trading to better allocate capital across their total portfolio, using objective, model-driven weightings derived from historical data, as well as deep statistical and mathematical concepts.
"AstraBit's implementation of MPT can help our members move beyond equal weighting or subjective allocation," said Nicholas Bentivoglio, CEO and Co-Founder at AstraBit. "AstraBit Portfolio aims to provide a risk-adjusted structure for users, working closely with their licensed financial professional, to allocate across diverse strategies and assets, based on actual performance relationships rather than intuition or static rules."
Institutional Theory, Adapted for Crypto
Modern Portfolio Theory, developed by economist Harry Markowitz, is a foundational principle in traditional finance for optimizing asset allocation. The theory provides a method for identifying the most efficient portfolio by balancing the expected return of each asset against its contribution to overall portfolio risk. AstraBit has adapted this model to evaluate digital assets and algorithmic trading strategies in the crypto market, treating each as a return-generating asset class.
The optimization engine calculates many components including, but not limited to expected return, variance, and covariance between assets, strategies, and even market indexes like the S&P 500 and the Astra100 Index. Based on this data, it calculates the capital weights that will result in things like the highest Sharpe or Sortino ratio, the lowest overall volatility, lowest downside deviation, etc., or a custom risk profile defined by the user. This approach can help users reduce overexposure to individual strategies and assets and introduces a quantitative discipline to bot portfolio construction.
Built for Practical Execution
The engine's functionality is designed to integrate directly with AstraBit's existing products and services. Users can select from strategies available on the platform, define constraints, and allow the engine to generate model-based allocations. These weightings can be implemented directly through the user's connected exchange accounts.
Key features include:
Portfolio optimization based on historical return and risk metrics
Correlation analysis across automated and manual trading strategies
Automated allocation and rebalancing recommendations
Compatibility with both centralized and decentralized exchanges
Unlike conventional applications of MPT that assume static asset classes, AstraBit's model incorporates variables specific to crypto trading. This includes the effect of exchange fees, slippage, bot behavior under different market regimes, and liquidity limitations across trading venues.
Enhancing Strategy Transparency and User Control
The availability of a quantitative allocation engine introduces an added layer of transparency for AstraBit users. Instead of allocating capital equally or based on perceived performance, traders can now make informed decisions grounded in statistical relationships between strategies. This is especially relevant in volatile or uncertain markets, where correlation clustering can lead to unintended concentration risks.
The tool benefits both discretionary and automated traders, including users of AstraBit's copy trading system and those building portfolios from the marketplace of available bots.
In addition to automated strategies, AstraBit enables comprehensive analysis of manual trades executed through connected exchanges. By integrating manual and algorithmic trading data into a single analytics view, users gain a holistic understanding of their entire portfolio performance. This unified perspective allows users to collaborate more effectively with licensed financial advisors to determine optimal strategy and asset allocations that align with their personal risk tolerance and return expectations.
Future Development
AstraBit is actively enhancing the optimization engine with additional layers of analytics, including forward-looking volatility modeling and integration of macroeconomic signals. There are also plans to support portfolio models that incorporate staking and yield-generating DeFi positions, broadening the use case beyond trading alone.
The Markowitz Strategy Engine is currently live and accessible via AstraBit's Portfolio Management interface.
About AstraBit
AstraBit is a U.S.-based, veteran-owned platform for automated crypto trading, DeFi staking, and portfolio management. It enables users to trade smarter using no-code bots, real-time analytics, multi-exchange connectivity, and a marketplace of expert strategies. AstraBit serves beginners, professionals, and institutions by delivering tools that prioritize transparency, control, and informed decision-making.
DISCLOSURE: AstraBit Portfolio and the Astra100X Index are informational tools designed to help users analyze digital asset portfolios and staking activity. They do not provide financial, investment, or tax advice, and outputs such as return estimates, volatility, or optimal allocations are hypothetical and not guaranteed. These tools rely on historical data and assumptions that may not reflect future market conditions. Past performance is not indicative of future results. All decisions related to trading, staking, and portfolio settings are the sole responsibility of the user. Digital assets are highly speculative and may involve significant risk of loss. Users should consult a licensed financial and tax advisor before making any investment decisions. AstraBit makes no guarantees of profit or performance.
Media Contact:
Cam Paulding
Chief Marketing Officer, AstraBit
[email protected]
SOURCE: AstraBit
View the original press release on ACCESS Newswire
J.Gomez--AT