How trading is evolving to its final stage — History and study case

June 4, 2025June 4, 2025 MelonopolyArticleLeave a Comment

Note: Article below is mirrored directly from Medium article found here.

Introduction

The act of identifying a need and offering a solution through exchange was an early spark of human economic creativity. Trading is more than just a transaction — it is the heartbeat of life’s evolution. Long before spreadsheets and order books, all living systems, from the roots of plants to the pulse of ancient cities, operated through exchanges that propelled survival, adaptation, and growth. Trade is not an invention, but an extension of a primordial rhythm — an energetic flow that transforms necessity into progress, forging the paths of civilization.

How Ancient Trade Changed the World

Trade created routes, ports and stories.

www.livescience.com

Trading: The Deep Roots of Exchange

Trading is far more than an economic practice — it is a manifestation of a foundational process that permeates all levels of life and the universe.

Trade and Mutualism in Nature

In the natural world, mutualistic exchanges are vital to survival and ecosystem resilience. For example, plants trade carbohydrates with mycorrhizal fungi in exchange for nutrients and water, forming expansive underground networks that benefit both partners.

“Mutualisms — interactions between species that provide reciprocal benefits — are a driving force in evolution and ecology, with mycorrhizal fungi–plant interactions among the most essential and widespread.”
(Bronstein, J.L., 2009, The evolution of mutualism, PubMed/Nature Reviews Genetics)

Exchange as a Universal Physical Principle

At the most fundamental level, the universe itself operates through exchange. All forces known to physics — from electromagnetism to gravitation — are described as interactions involving the transfer of energy, momentum, or other properties.

“Fundamental interactions are processes by which elementary particles exchange energy and change state… forming the basis for all the forces in nature.”
(Wikipedia: Fundamental interaction)

Social Reciprocity: The Roots of Human Trade

In human societies, trade formalized the principle of reciprocal benefit that is observed in nature and physics. Exchanging goods, ideas, or services is at the heart of economic and cultural evolution, making cooperation and specialization possible.

“Biological markets provide a framework for understanding how individuals trade commodities, negotiate cooperation, and adjust strategies based on supply and demand, much like human economies.”
(Hammerstein, P. and Noë, R., 2022, Biological trade and markets, Springer/Philosophy of Science)

Milestones: The Historical Arc of Trading

Human history is, in many ways, the story of trade. Some of the world’s most pivotal moments were born at trading crossroads:

Ancient Sumer & the Advent of Writing: Around 3500 BCE, Sumerians in Mesopotamia invented cuneiform writing — initially to record trade transactions. This leap allowed civilizations to track assets, debts, and agreements, providing stability and scale.
Babylon & Commercial Law: The Babylonians institutionalized commerce, developing early banking systems and comprehensive codes for contracts, further enabling complex trade networks.
The Silk Road & Global Links: Trade routes connected East and West, moving not just silk and spices but ideas, religions, and technologies — showing trade’s role as a diffuser of progress.
Opium Wars & Colonial Hegemony: In the 19th century, the British Empire’s trade policies — and ensuing conflicts like the Opium Wars — reshaped economies and destinies, underlining how trade could trigger epochal shifts.
The Rise of Stock Exchanges & Globalization: From Amsterdam’s 17th-century exchange to today’s digital platforms, the mechanisms of trading became faster and more open, re-configuring who could participate and innovate.

Trade, again and again, has altered the fate of nations and individuals — its reverberations can be felt in every era’s turning point.

https://en.wikipedia.org/wiki/Timeline_of_international_trade

The Cycle of Civilizations and Markets

Patterns of growth, peak, and decline are woven into the very fabric of civilization, and these cycles often mirror those of the natural environment. Earth itself experiences climatic phases, such as the Medieval Warm Period and the Little Ice Age, that have dovetailed with eras of prosperity and periods of hardship in human societies.

Historically, civilizations rose as trade expanded and innovations flourished — only to face decline when resources dwindled, climates shifted, or external competition intensified. Empires from Rome to the British and Dutch expanded on the tides of commerce, projecting influence across continents.

Compression in the Last Century: Civilization at Fast-Forward

Yet in the last hundred years, the pattern has undergone dramatic “compression” — the time between periods of growth, dominance, peak, and decline has rapidly shortened. Where ancient empires or market eras once lasted centuries, today’s advances can catapult nations, industries, or companies from ascendance to obsolescence in mere decades, sometimes even years.

This historic compression is driven by several key forces:

Technological Advancements: Innovations such as the telegraph, telephone, internet, and now artificial intelligence have eliminated geographic and temporal barriers. Information and capital now move instantly across the globe, accelerating both opportunity and disruption.
Market Integration: Globalization has fused what were once isolated economies into a single, highly reactive network. Economic shocks, booms, and crises transmit worldwide in moments, amplifying both growth and volatility.
Acceleration of Innovation: Breakthroughs now build upon each other exponentially. For example, Moore’s Law in computing shows how technological progress stacks rapidly, making competition more intense and strategic cycles shorter.
Financialization and Speed: The rise of complex derivatives, leveraged financial products, and high-frequency trading means that market bubbles and corrections can form and burst at unprecedented speeds.

Consider that the average lifespan of a company on the S&P 500 has plunged from about 60 years in the 1950s to under 20 years today. Economic renaissances like Japan’s postwar rise, the tech boom in Silicon Valley, or China’s leap after the 1980s have unfolded at a breakneck pace compared to earlier hegemonies. Recent market crises — such as the 2008 financial collapse or the COVID-19 freefall and rapid recovery — played out in days or weeks, not years or decades.

Compression reflects an era in which cycles of growth, dominance, decline, and renewal are happening faster than ever before. For traders and innovators, this means heightened risks but also extraordinary opportunities: the future belongs to those who can recognize and adapt to accelerating change.

The Digital, Crypto & AI Trading Revolution

The past century marks a quantum leap in the evolution of trading. Communication technologies and digitization have dissolved physical borders, enabling transactions in milliseconds across the globe. The introduction of computers, the internet, and sophisticated algorithms transformed trading from floor shouting to silent streams of code executing billions in value each second.

More recently, artificial intelligence and quantum computing have redefined what’s possible. Markets today are arenas of data — an environment where edge belongs to those who can extract insight and act at lightning speed. The rise of digital platforms, algorithmic strategies, and AI-driven decision-making is not simply an upgrade; it is a paradigm shift. Trading has become a science of anticipating, adapting, and mastering information flow — ushering us into a new era.

“Agentic AI systems can independently formulate goals, devise strategies, and execute decisions in complex market environments, rapidly adapting to new information and reshaping traditional financial operations.”

The Crypto Revolution: Blockchain and Digital Assets

Within this digital revolution, the rise of cryptocurrency and blockchain technology represents another paradigm shift. Blockchain enables trustless, peer-to-peer exchanges via distributed ledgers, decentralizing control and adding transparency and security to financial systems. Since Bitcoin’s launch in 2009, digital assets have multiplied, from smart contract blockchains like Ethereum to DeFi protocols reshaping lending, trading, and investing.

“Blockchain is a disruptive technology that — when deployed responsibly — can empower users, reduce corruption and increase trust. Cryptocurrencies built on distributed ledger technology (DLT) have emerged as potential gateways to new wealth creation and disrupters across financial markets.”
— World Economic Forum, What is blockchain and how does it work?

Crypto markets operate 24/7 on a global scale, creating unprecedented conditions for automation and real-time data-driven trading. Algorithmic and AI-powered models are now as common in crypto as in stocks or forex, managing high-frequency trading and responding instantly to new information.

“Algorithmic trading, which relies heavily on programmed instructions, already dominates global markets. It accounts for 60%-73% of equities trading on U.S. markets, 60% in Europe and 45% in the Asia Pacific”
— Benzinga, What Percentage of Stock Trades Are Made by Bots and Algorithms?

Major trading platforms are expanding their focus to include cryptocurrencies, combining the adaptive power of AI with the transparency and programmability of blockchain. The convergence of AI and crypto signals a future financial ecosystem that is globally accessible, continuously evolving, and increasingly autonomous.

Case Study: EPIC Agentic AI as the Final Stage

Few innovations capture this final evolutionary stage like EPIC Agentic AI. Emerging from the competitive crucible of crude oil futures, EPIC was engineered to do more than automate trades — it was designed to autonomously evolve, outthink, and outperform in the world’s most volatile arenas.

1. Origins and Core Philosophy

Developed since 2016, EPIC AI started with the vision of mastering unpredictable markets. The foundational breakthrough was the integration of Agentic AI — enabling the system to autonomously adapt and refine its tactics in real time. As a result, EPIC quickly scaled beyond oil into equity indices, cryptocurrencies, and more.

2. Architecture: Swarms and Self-Learning

At the heart of EPIC’s edge is its multi-agent “swarm” architecture. These autonomous agents specialize in everything from market selection and order flow analysis to risk management. The system is continuously trained — subjected to extreme simulated and real-world conditions — to expose, then eliminate, any weakness. Retraining, reinforced learning, and deep learning techniques fuel continuous improvement. EPIC’s proprietary models weave together more than 300 algorithms, validated by years of live and historical data.

A defining feature — EPIC IDENT™ — acts as a real-time order flow intelligence system. It tracks the behavior and “fingerprints” of market participants (especially automated trading systems), using historical patterns, volatility, and liquidity dynamics to predict and capitalize on price movements. EPIC IDENT™ is a live, adaptive successor to back-testing — foreseeing pivots as they emerge.

3. Risk Controls and Performance

Risk management is foundational. The system enforces strict drawdown limits, utilizes multi-layered checks, and dynamically adjusts position sizes. Results speak for themselves: win/loss ratios exceeding 7:1, consistent annualized returns, and robust out-performance of legendary funds like Simons’ Medallion, Lynch, and Buffett.

Who Is Jim Simons?

Jim Simons is a renowned mathematician and investor. Known as the “Quant King,” he is the founder of Renaissance…

www.investopedia.com

4. Scientific Foundations and Innovation

EPIC’s conceptual roots in mathematical physics — in particular, the 3–6–9 pattern found in string theory and quantum teleportation — inform the structural elegance of EPIC’s architecture. Just as string theory suggests hidden dimensions and interconnectedness, EPIC’s multi-layered system synthesizes data across timecycles and market states, acting as a hyper-dimensional map for trading opportunity.

The 3–6–9 pattern is unusually prominent in string theory’s dimensional structure: 3: Observed spatial dimensions and complex dimensions of Calabi-Yau manifolds. 6: Compactified spatial dimensions in 10D superstring theory. 9: Total spatial dimensions in superstring theory.

What Sets EPIC Apart

EPIC isn’t just another quant system — it heralds a new era. Its full autonomy, relentless self-optimization, and capacity for risk-controlled alpha generation place it ahead of traditional quant and even the most sophisticated high-frequency trading desks.

The ecosystem is decentralized: clients maintain custody and control, using EPIC as a plug-in to their existing accounts — no pooled funds, no opaque management, just transparent, user-driven AI execution. Its modular architecture allows for easy scaling and cross-asset deployment.

EPIC’s approach is emblematic of where finance is headed: Agentic, adaptive, and democratized for those able to harness advanced intelligence.

Conclusion: The Future of Trading — Cycles, Intelligence, and Autonomy

From the primal energy exchanges of the natural world to the neural nodes of AI agent swarms, the story of trading is one of perpetual evolution. Each epoch — each technological leap — compresses time, increases complexity, and rewards those who can read patterns in the chaos.

EPIC Agentic AI stands at the frontier, synthesizing history, science, and innovation into a living testament of what trading can become: fully autonomous, risk-aware, and ever-evolving. As we enter a new era, the question isn’t just how we will trade — it’s how we will evolve alongside these intelligent systems, riding the next great cycle in the relentless march of progress.

References

Foundations of Trade and Scientific Insight
Bronstein, J.L. (2009). The evolution of mutualism, Nature Reviews Genetics.
Fundamental interaction (Wikipedia)
Hammerstein, P., & Noë, R. (2022). Biological trade and markets, Philosophy of Science.
Timeline of international trade (Wikipedia)

On Agentic AI, Quantitative Finance, and Industry Impact

EPIC Agentic AI — Official Resources and Deep Dives

This article is for informational purposes only and does not constitute financial or investment advice.

How We Train AI Agent Swarms to Trade

May 20, 2025May 20, 2025 MelonopolyAbout

EPIC Agentic AI (epicaihub.io), is a specialized trading software that uses autonomous AI agent swarms to execute trades with minimal human intervention, focusing on markets like crude oil, NASDAQ, crypto, and soon to be released Equity Basket of Stocks with a recently reported win/loss ratio of 7.6:1, significantly higher than typical quant trading systems (1.5:1 to 5:1).

EPIC AI’s training involves pushing AI agents to their limits, identifying failure points, and retraining them to enhance ROI while maintaining risk mitigation, demonstrating strong performance metrics such as closing intraday crude oil trades with gains of +756 and +1417 ticks in yesterday’s trading session.

The software leverages predictive analytics and rapid execution, unlike general AI which solves multi-step problems across various domains, and it has actively traded volatile markets, noting a pivot to net short on NQ last week, with further details available on our website, X feeds and performance dashboard.

The process described in the context of EPIC AI’s training is a common approach in the AI industry, particularly for developing and optimizing trading systems. Here’s a general explanation of this process:

1. Pushing Agents to Their Limits

Concept: AI agents, in this case, are autonomous entities within the system designed to perform specific tasks, such as executing trades based on predefined algorithms and market data.

Process: These agents are tested under extreme conditions to understand their performance boundaries. This involves simulating high-volatility scenarios, large data inputs, and rapid decision-making environments to see how they handle stress and complexity.

Purpose: The goal is to identify the maximum capacity and potential weaknesses of the agents, ensuring they can operate effectively under real-world market conditions.

2. Identifying Failure Points

Concept: Failure points are scenarios where the AI agents underperform, make incorrect decisions, or fail to meet expected outcomes.

Process: During testing, the system monitors the agents’ performance metrics, such as accuracy, speed, and profitability. Any deviations from the desired outcomes are logged and analyzed.

Purpose: Understanding these failure points is crucial for improving the system. It helps in pinpointing specific algorithms, data inputs, or decision-making processes that need refinement.

3. Retraining to Enhance ROI and Maintain Risk Mitigation

Concept: Retraining involves updating the AI models with new data, adjusting algorithms, and fine-tuning parameters to improve performance.

Process: Data Analysis: The failure points and performance data are used to identify patterns or errors in the AI’s decision-making process.

Algorithm Adjustment: The underlying algorithms are modified to address these issues. This might involve changing weightings in machine learning models, updating trading strategies, or incorporating new risk management rules.

Simulation and Testing: The retrained agents are then tested again in simulated environments to ensure improvements.

Purpose: The aim is to enhance the Return on Investment (ROI) by improving the accuracy and profitability of trades while also maintaining risk mitigation strategies to protect against significant losses.

4. Demonstrating Strong Performance Metrics

Concept: Performance metrics are quantitative measures that evaluate the success of the AI system, such as trade gains, win/loss ratios, and risk-adjusted returns.

Process: In the case of EPIC AI, the system demonstrated strong performance by closing intraday crude oil trades yesterday with significant gains (+756 and +1417 ticks). This indicates that the retraining and optimization efforts were successful.

Purpose: These metrics serve as evidence of the system’s effectiveness and are used to validate the training process and attract potential users or investors.

General Industry Context

Iterative Development: This process is iterative, meaning it involves continuous cycles of testing, failure analysis, and improvement. In the AI industry, particularly for trading systems, this is essential due to the dynamic nature of financial markets.

Risk Management: A key focus is on balancing potential gains with risk mitigation, as excessive risk can lead to substantial losses, undermining the system’s reliability.

Data-Driven Decisions: The entire process relies heavily on data. Large datasets are used for training, and real-time data is crucial for ongoing performance and adjustments.

Specialization: While the process is general, the specifics (e.g., the types of agents, the markets targeted, and the performance metrics) are tailored to the particular application, in this case, trading in volatile markets like crude oil, crypto, stocks and NASDAQ.

Example in Trading

Initial Training: Agents are trained on historical market data to recognize patterns and make predictions.

Stress Testing: They are then exposed to simulated extreme market conditions to identify weaknesses.

Failure Analysis: If an agent consistently fails to predict a market downturn, the system analyzes why (e.g., inadequate data, flawed algorithm).

Retraining: The agent is retrained with additional data or a revised algorithm, incorporating new risk management protocols.

Performance Evaluation: The improved agent is deployed, and its performance (e.g., +756 ticks gain) is measured against benchmarks.

This rigorous, data-driven approach ensures that AI systems like EPIC AI can adapt to changing market conditions, improve over time, and deliver consistent results, which is critical for maintaining trust and effectiveness in the trading industry.

Summary: How EPIC AI is Accomplished

Building a system like EPIC AI requires a combination of advanced technology, data, and expertise. The following components are involved:

Data Infrastructure:

High-quality real-time and historical market data is essential, sourced from exchanges and APIs.

Algorithmic Framework:

Reinforcement learning is central, with agents learning from simulated trades. Deep learning techniques, such as neural networks are used for pattern recognition.

Multi-agent systems, where agents specialize in tasks like market selection and trade execution, align with designs in Designing a Detailed Multi-Agent Trading System Using AI.

Computational Resources:

Powerful computing infrastructure, such as GPUs and cloud platforms, are needed for processing large datasets and running simulations.

Risk Management:

Robust risk controls, such as stop-loss limits and position sizing, are critical, especially for volatile markets. EPIC’s focus on risk mitigation involves highly sophisticated integrated risk management protocols.

Continuous Improvement:

Ongoing monitoring and retraining ensure adaptability. For instance, EPIC’s updates, like v3 and v4, indicate a process of iterative enhancement

Integration with Trading Platforms:

Seamless integration with brokers and trading platforms via APIs is necessary for real-time execution.

Conclusion

Training AI agents for trading, as seen in systems like EPIC AI, involves a rigorous, data-driven process centered on reinforcement learning, stress testing, and continuous improvement.

EPIC AI, developed by Compound Trading Group, exemplifies this with its autonomous agent swarms, achieving exceptional results in markets like crude oil, NASDAQ, crypto and stocks.

Accomplishing such a system requires advanced data, algorithms, and computational resources, with ongoing monitoring to adapt to market changes.

While exact methods are proprietary, EPIC AI deploys a robust, multi-agent architecture that aligns with advanced Agentic AI industry trends.

EPIC Agentic Ai Trading Software Agent Swarms Training

Performance Report WTI Crude Oil Futures CL – Velocity Protocol Live Trading Results: Feb 25 – May 8, 2025 | $1M Account

May 20, 2025May 20, 2025 MelonopolyPerformance

Performance Report

WTI Crude Oil Futures CL – Velocity Protocol

Live Trading Results: Feb 25 – May 8, 2025 | $1M Account

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Introduction

Epic Agentic AI is a cutting-edge autonomous trading platform powered by self-learning AI agents that optimize trading strategies in real time. Designed for accredited investors and institutional funds, our system leverages deep reinforcement learning, multi-agent collaboration, and ultra-low latency execution to deliver consistent, high-risk-adjusted returns across global markets.

Now spans 27 financial instruments (stocks, commodities, crypto), delivering baseline expected 80%+ annualized returns (https://epicaihub.io/epic-agentic-ai-white-paper/).

Autonomous decision-making processes 9,300+ weighted decisions instantly, adapting to real-time market shifts.

Clients retain full custody of funds and opt-in/out control, ensuring transparency and trust.

EPIC IDENT™ Order Flow and 300+ custom models enable tailored strategies for diverse accredited investors and institutional fund portfolios.

Scalable for account sizes ($200K–$50M+ for crude oil futures), accommodating varying accredited investors and institutional funds needs.

Custom tailored innovative, high-performance solutions, outpacing competitors using static models ([PwC, 2025]).

Enhance operational efficiency by automating trade execution and compliance, freeing resources for client relationships.

Positioned as a market leader with EPIC’s 2025 private equity deployment and 2026 public market expansion.

Firms without AI risk losing clients to tech-forward competitors in volatile markets ([AdvisorHub, 2025]).

Actual Trading Results

Epic Agentic AI leverages self-optimizing Agentic AI to conquer volatile markets, starting with crude oil futures. Our system outperforms top quant funds by 4–5x (currently on pace for 98% annualized ROI as of May 8, 2025) while maintaining elite risk control (Sortino 10.74)—blending venture capital returns with the safety of Treasury bonds

Performance Highlights (as of May 8, 2025)

Metric	Epic AI	Top Quant Funds	S&P 500	Epic vs. Benchmarks
Annualized ROI	98.0%	18–25%	10–12%	4–5x higher returns
Sharpe Ratio	11.7	3.0–3.5	0.8–1.2	3.3x more efficient
Sortino Ratio	10.74	3–5	0.8–1.2	2–3x better downside control

Results

Metrics

Metric	Epic AI	Top Quant Funds	S&P 500	Epic vs. Benchmarks
Win Rate (Sequences)	88.4% (61/69)	55–65%	Approx 63% of months have positive returns since 1999	23% higher win rate
Win/Loss Ratio	7.6:1	1.5:1 – 5:1 (varies)	0.63:1 (approx.)	Consistency in wins
Max Drawdown	-9.0%	-10–15%	-49% (2000), 33% (2020), 27% (2022)	Elite risk management and backfill protocol to rapidly make back losses
Avg Loss (Sequences)	-0.6%* (excl. 9% outlier)	-2–3%	-4-5% (average drawdown in a red month since 1999)	5x smaller losses
Profit Factor	2.09			>2 is a robust strategy

* Average of the 7 other losing sequences not including the -9% loss (all <1%).

Excess Returns	Epic AI	T-Bills	S&P 500	Epic vs. Benchmarks
72-Day Return	14.42%	0.97%	9.42%	Same risk higher returns
Annualized Return	98%	5%	11%	Lower Risk Higher Returns

Max drawdowns and ROI on an account depend on how much margin is available per contract. Adjustments can be made to both based on an investor’s risk tolerance. Sample size remains small as of May 8 as we continue our live data gathering.

Competitive Positioning

We combine the high returns of venture capital (98% annualized) with the risk profile of Treasury bonds (Sortino 10.74). Even with the 9% drawdown in early May, our Sharpe of 11.7 is unmatched in institutional finance.”

Execution and Risk Metrics

Metric	Epic AI	Comment
Days/Trade	5.03
Average Hold Time	902 Minutes
Fill Rate	100%
Market Impact	$500M	Conservative estimates suggest $500M AUM with no decay
Volatility	~27%	**
Leverage	Futures contracts	Leverage is built into the futures contract and has several variabilities

** Epic Agentic AI’s estimated annualized volatility is ~27%, reflecting its aggressive alpha generation. Controlled by a Sharpe Ratio of 11.7 and max drawdown since the introduction of agentic AI of 9.0%, it balances risk/reward for accredited investors. **

Key Expectations for Performance

May 19, 2025May 23, 2025 MelonopolyAbout

Key Expectations for Performance:

EPIC Agentic AI Oil Trading Software

To help you navigate market volatility with confidence, this document outlines the key performance expectations and insights into the trading approach of the EPIC Agentic AI Oil Trading Software. Understanding these principles will prepare you psychologically for the journey ahead and align your perspective with EPIC’s long-term strategy.

Position Trading Principles

EPIC employs a position trading strategy, utilizing multiple entries and exits to build optimal positions when trading in trending markets. This approach enables EPIC to capitalize on market trends by gradually scaling into positions and dynamically adjusting as market conditions evolve. While it may seem counterintuitive—particularly when EPIC takes positions that appear to oppose a market move—this is an intensely calculated method. The AI optimizes the probability of success by managing trades to minimize risk and maximize gains over time, even in volatile or adverse market conditions.

Cyclical Nature of Trading

EPIC’s trading performance follows a cyclical pattern driven by its continuous AI learning, development, and the inherent cyclical nature of markets. The software is designed to push the boundaries of profitability, often resulting in a parabolic-shaped returns graph during periods of aggressive trading. This occurs as EPIC optimizes its strategies to maximize gains within a cycle. However, when the system reaches the limits of a cycle, it may encounter its largest losses, as the AI tests the boundaries of market conditions. Following such periods, EPIC reduces its aggressiveness, recalibrates, and begins a new cycle of growth. This adaptive process reflects the software’s ability to learn, improve, and push the limits of what is possible.

Additionally, markets themselves exhibit a cyclical nature in terms of the structures and order flows that EPIC relies on to achieve its elite level of success. When these market structures and flows are intact, EPIC can dominate with outsized wins, leveraging its AI to capitalize on optimal conditions. During periods when market structure and order flow are less than ideal, EPIC may experience losses or adopt a more conservative approach, effectively taking the foot off the gas while waiting for favorable conditions to return. Through all market cycles, EPIC’s best-in-class risk management ensures that potential downsides are carefully controlled, protecting your account while positioning for future opportunities.

Implication for Account Management: When deciding when to start accounts or add or remove funds, consider both EPIC’s internal trading cycles and the cyclical nature of market conditions.

Periods of parabolic gains or strong market structures may be followed by drawdowns or quieter phases, while recalibration periods may offer more stable entry points. Staying mindful of these cycles can help you align your account decisions with EPIC’s dynamic behavior.

Drawdowns Are Normal

At any given time, drawdowns of 10-15% are within expected ranges, with larger drawdowns possible at the peak of a trading cycle or during suboptimal market conditions. These temporary declines in account value are a natural part of trading and do not indicate system failure. The position trading strategy and cyclical nature of EPIC’s approach may contribute to short-term fluctuations as the AI navigates all market conditions to secure advantageous positions.

Losses on Individual Trades Are Expected

EPIC’s strength lies in generating positive returns over many trades, not in winning every single one. The use of multiple entries and exits, combined with the cyclical push for maximum gains and varying market conditions, means some trades—particularly at cycle peaks or during disrupted market structures—may close at a loss as part of the broader strategy to achieve an optimal position. These losses are integral to the system’s calculated and adaptive approach.

Long-Term Advantage

The software’s AI is engineered to deliver a mathematical edge, ensuring profitability over extended time frames when used as intended. By leveraging the inherent mathematics of markets, sophisticated position management, continuous learning, and best-in-class risk management, EPIC achieves consistent, elite performance. The system is designed for the long haul, targeting annual returns of 80% or higher, though results depend on market conditions, user settings, and the cyclical phase.

Key Tip

Avoid focusing on daily or weekly results, as short-term fluctuations, cyclical peaks, and varying market conditions are expected. Instead, review your performance on a monthly or quarterly basis to align with EPIC’s long-term, position-based, and cyclical strategy. Trust the AI’s calculated, adaptive, and risk-managed approach, even when short-term market moves, cycle transitions, or market structure disruptions seem unexpected. When planning account changes, such as starting, adding, or removing funds, consider consulting with your EPIC representative to discuss the current cycle phase and market conditions.

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Trade Instruments Comparison

May 7, 2025May 23, 2025 MelonopolyAbout

Instrument Comparison

Welcome to EPIC Agentic AI, where advanced automation powers strategic trading. This document outlines the pros and cons of three key trade instruments—Oil, Nasdaq, and Cryptocurrency—offered through our proprietary Velocity Protocols. Each instrument offers unique opportunities and risks, designed for different investor profiles. Use this guide to select the option that best aligns with your financial goals.

Overview of Trade Instruments

EPIC’s trading software leverages cutting-edge AI to optimize returns across three instruments: Oil, Nasdaq, and Cryptocurrency (BTC, SUI, SOL, XRP, TAO, ETH). Below, we compare their key features, including return potential, volatility, activity levels, and entry requirements.

Instrument	Minimum Account Size	Return Potential	Volatility	Market Activity	Key Advantage	Key Drawback
Oil	$100,000	Highest	High	Almost always active	Maximizes ROI	Significant swings/drawdowns
Nasdaq	$200,000	High	Low	Consistently active	Stable performance	High entry barrier, lower ROI than oil
Crypto	$25,000	Moderately High	Medium	Variable (can be inactive)	Low entry barrier	Periods of inactivity

Detailed Analysis

Oil Velocity Protocol

Pros:

Highest ROI Potential: Offers the greatest return on investment among EPIC’s protocols, ideal for aggressive investors.
Constant Market Activity: The oil market is nearly always active, ensuring your capital is consistently engaged.

Cons:
- High Volatility: Significant price swings and drawdowns require a strong risk tolerance.
- Entry Barrier: A $100,000 minimum account size limits accessibility for some investors.
Best For: High-net-worth investors seeking maximum returns and comfortable with volatility.

Nasdaq Velocity Protocol

Pros:

High Return Potential: Delivers strong returns, appealing to investors seeking robust growth with less volatility than Oil.
Low Volatility: Provides a stable trading experience with fewer dramatic swings.
Consistent Activity: The Nasdaq market offers reliable data for EPIC to execute trades consistently.

Cons:
- High Entry Barrier: Requires a $200,000 minimum account, the highest among the protocols.
Best For: Investors prioritizing stability and strong returns but with significant capital to deploy.

Crypto Velocity Protocol (BTC, SUI, SOL, XRP, TAO, ETH)

Pros:

Low Entry Barrier: A $25,000 minimum account makes this protocol accessible to a broader range of investors.
Moderately High Return Potential: Offers attractive returns during active market periods, balancing risk and reward.

Cons:
- Variable Activity: Extended periods of low market activity may result in EPIC holding no positions, pausing returns.
- Medium Volatility: More volatile than Nasdaq but less than Oil, requiring moderate risk tolerance.
Best For: Investors with smaller capital pools seeking strong returns and willing to accept occasional inactivity.

Why Choose EPIC Agentic AI?

EPIC’s automated trading software uses advanced AI to analyze market data and execute trades with precision. Each Velocity Protocol is optimized for its respective instrument, offering tailored solutions for diverse investor needs. Whether you prioritize maximum returns, strong growth with stability, or accessibility, EPIC has a solution for you.

Next Steps

Ready to explore EPIC’s trading solutions? Contact our team at contact@epicaihub.io to discuss which Velocity Protocol aligns with your investment goals. Visit https://epicaihub.io/ for more details on account setup and performance metrics.

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