Algorithmic Trading Archives - Coinrule

How AI Transformed Algorithmic Trading

Gabriele Musella — Wed, 04 Mar 2026 12:22:10 +0000

How automation evolved from terminals to AI agents that can adapt and operate to improve your trading experience and outcome.

Why this matters now

Markets are undergoing a structural change. Execution has become electronic by default, information travels instantly, and decision cycles are collapsing from hours to seconds. In that environment, discretionary trading becomes harder to sustain because the trader is forced to react faster than human cognition comfortably allows. The modern alternative is a rules first approach, and increasingly, an agent supervised approach, where decision logic is explicit, execution is continuous, and humans stay in control through guardrails.

This article explains how automated trading works, how it developed historically, what changes when AI enters the workflow, and what an agent framework actually means in practical terms.

What “Algo Trading” really is

At its core, automated trading is a rule based system that places trades on your behalf. The rules encode decisions that you have already made before the market pressure arrives. The objective is not sophistication for its own sake. The objective is discipline.

A classic principle in rule driven trading is simple: never enter a position if you do not already know how and when you will exit. In practice, this means you define your conditions and thresholds before volatility forces you into emotional decision making.

A useful way to think about automated trading is that it replaces reactive judgment with pre committed logic. That is why it appeals to traders during high volatility: it reduces panic and increases rational decision making. Platforms like Coinrule exist to translate these rule based ideas into executable systems, allowing traders to convert strategy logic into live rules without needing to build the infrastructure themselves.

The historical arc: how we got here

Modern automation did not start with crypto. It started when markets turned electronic.

1) From open outcry to terminals

Markets began in physical exchanges where orders were negotiated face to face. Execution was human, slow, and visibly social.

When terminals arrived, everything changed. The screen became the market interface. That interface also hid a great deal: spread dynamics, microstructure effects, and the reality that a single order can trigger many forms of price movement. This transition marked the beginning of a world where execution is mediated by technology rather than people.

2) ETFs made baskets tradable at scale

A major shift came with ETFs, which made baskets of assets tradable like single instruments. Early ETFs were more discretionary in how they were managed. Over time, the construction and management of many ETFs became increasingly rules driven, reflecting the broader trend toward automation and repeatable logic.

The important concept is not “ETF” itself. The important concept is modularity: a strategy can be applied to a basket, not just a single asset. Once that becomes normal, trading becomes less about picking one instrument and more about designing decision systems for categories of exposure.

3) Mobile apps and the retail access wave

As app stores and mobile platforms exploded, retail investors gained direct access to instruments that previously required intermediaries. The market also saw the rise of fractional shares, and later, the idea that execution could be simplified into a product experience.

Copy trading emerged in that era as a primitive form of automation: you mirror someone else’s actions. It is automated execution, but the logic is outsourced to another person.

4) No code logic builders

The next step was letting retail investors build their own logic without programming. In computer science terms, an algorithm is a set of instructions and conditional decisions. Translating that into trading means you can define conditions using indicators and triggers, then execute across timeframes. Whether you implement it in Python, C++, or a visual interface, the underlying concept is the same: structured decision logic operating continuously.

What changes when AI enters the workflow

Traditional automation often behaves like a static block of code. It executes the same logic regardless of context unless you explicitly programmed every exception. AI changes the workflow at each stage, not because it makes you “smarter,” but because it changes the cost of iteration and the breadth of what can be operationalized.

A useful way to frame the shift is that AI reduces the gap between institutional and non institutional capability. Historically, institutions had developer teams and tooling that retail did not. With AI and agents, parts of that capability become accessible without requiring a full engineering organization.

The old workflow

The traditional automation workflow tends to look like this:

You come up with an idea
You formalize it into code or a fixed framework
You backtest on historical data
You deploy it and it runs as written
You analyze performance later and make manual edits

This flow works, but it is rigid. If the world changes, the strategy does not naturally adapt.

The AI enabled workflow

AI reshapes the process across the entire lifecycle:

You brainstorm and iterate with an LLM during ideation from the UI via Coinrule MCP
The LLM helps you draft scripts or structured logic
You test and evaluate faster across scenarios
You run strategies while capturing detailed execution context
You feed that context back into the model to improve the next iteration

The important shift is that the strategy becomes adaptable in concept, even if you still apply strict guardrails in practice. The goal is not infinite flexibility. The goal is controlled adaptability.

The four stage framework: ideation, build, execution, analysis

A clear way to understand AI assisted trading is to break it into four stages.

1) Ideation

AI helps you iterate faster, but the edge still needs to come from you. The model can list every imaginable strategy, but that is not the same as finding a strategy that fits your risk appetite, time horizon, and market familiarity.

Your job is to provide constraints and context, including:

goals and risk appetite
markets you understand enough to validate outputs
realistic assumptions about liquidity and volatility

A critical warning worth keeping: you cannot outsource your brain to the LLM. Treat it as a sparring partner, not a money machine.

2) Script generation or rule design

AI can produce code, Pine scripts, and structured logic quickly. It can also help you create a test harness and compare behavior against historical data.

However, backtests are not reality. They are a simplified simulation. The model can help generate the logic, but you must evaluate whether it is coherent and robust.

3) Execution

Execution is where many strategies fail, because live markets introduce friction:

slippage
fees
partial fills
missed triggers
adverse selection from faster bots

This is why platforms matter: building a strategy is one task, building a production execution environment is another. If you focus on strategy design, you generally want to be exchange agnostic and avoid rebuilding the entire infrastructure stack yourself. Platforms like Coinrule are built to keep you exchange agnostic while handling the operational details that break most strategies in production, so you can focus on the logic and the learning loop rather than rebuilding execution plumbing.

4) Analysis and optimization

This is where AI becomes genuinely powerful. If you capture the right context about execution, you can feed it back to the LLM so it can explain what happened and propose refinements.

What context matters most is the unglamorous reality:

was there slippage
did the limit order fail to fill
was there a data issue
was there a macro event you did not account for
did the strategy break under a specific microstructure condition

If you give the model real execution context, it can learn in a grounded way. Without that context, it will overfit to theories. This shift is particularly powerful when combined with platforms like Coinrule that already provide execution infrastructure, allowing traders to focus on strategy generation while automation handles deployment and monitoring.

From bots to agents: what “agent trading” implies

A “bot” is usually a single strategy running a fixed set of rules. An “agent” implies a broader capability: a system that can observe, retain memory, consult tools, and coordinate with other agents under a governance framework.

Our future vision is a “set of agents around you,” each with a specialist role, such as:

analyst agent for research and synthesis
trading agent for execution decisions within constraints
engineering agent for tooling and integration
data agent for market data and external signals

The key concept is not autonomy for its own sake. The key concept is division of labor and supervised decision making.

Human in the loop is not optional

A credible agent framework is designed for shared control. Humans define thresholds, risk boundaries, and escalation rules. Agents operate within those parameters, and consult the human when decisions exceed predefined limits.

One practical example is threshold based delegation: an agent may be allowed to experiment with positions below a certain fraction of portfolio size, but must provide a summary and request confirmation above that threshold.

This is how real systems become scalable without becoming reckless.

AI concepts explained plainly

Here is what several, relevant technical ideas mean in practical trading terms.

MCP, API, CLI

These refer to how systems connect and how power users configure workflows.

API is the standard interface to send and receive market and order data
CLI is a command line interface, usually used by advanced users to automate configuration and operations
MCP was referenced as part of tooling and connector architecture across platforms, meaning an environment where tools and connectors are coordinated rather than handled manually

The practical takeaway is simple: agent systems are only as good as the tools they can reliably access.

Fine tuning

Fine tuning means refining a model using specific data so it behaves better in a target domain. In trading, the most valuable fine tuning is not generic market theory. It is the execution reality of a given platform, instrument set, and user behavior constraints.

RAG context ready

Retrieval augmented generation means the model can pull relevant context from stored data when making decisions. In trading, that can include:

prior strategy behavior in similar volatility
execution logs showing typical slippage and fill patterns
a record of macro events that broke strategies

The value is not “knowledge.” The value is situational recall.

Strategy performance memory retention

This means the system preserves what happened across runs so it can learn iteratively, rather than treating each strategy run as disconnected. In real markets, this is essential because performance is path dependent, and the difference between backtest and live execution is often where the alpha is lost.

Financial guardrailing

Guardrails are explicit constraints that prevent runaway behavior. They are the core of safety in automated systems. Examples include:

maximum position size
maximum daily loss
maximum number of trades per hour
allowed instruments list
required confirmation for large changes

Guardrails turn “automation” into “controlled automation.”

What good practice looks like

If you want a durable approach to AI assisted automated trading, read this set of principles that are worth adopting:

Pre commit your exits, not just your entries
Treat AI as a collaborator, not a replacement for judgment
Prefer real execution data over backtest narratives
Capture detailed logs and feed them back into analysis
Use thresholds and escalation for human oversight
Separate strategy design from execution infrastructure
Run a portfolio of strategies for different regimes rather than betting on one idea forever

Automated trading began as rule based execution designed to remove emotion and impose discipline. Over time, markets became electronic, retail access expanded, and no code tools made logic creation easier. AI now changes the economics of the workflow by accelerating iteration and enabling agent frameworks that retain memory and operate continuously with guardrails.

If you understand one thing, it should be this: the edge is no longer purely in writing code. The edge is in building a controlled system that can learn from live execution, retain what it learned, and operate under constraints that you trust.

The post How AI Transformed Algorithmic Trading appeared first on Coinrule.

Building the Future of Trading Automation: Inside Coinrule’s Next Chapter

Gabriele Musella — Tue, 11 Nov 2025 17:36:25 +0000

https://youtu.be/YitEyKmx8fg?si=PSML7Qzg3Wi_LBSL

After years of relentless iteration, Coinrule has entered a new phase, one defined by discipline, focus, and profitability.

The team has streamlined operations, reduced overhead, and reached near break-even while continuing to ship new products that expand the boundaries of what automated trading can be.

“We went down to profitability,” says Gabriele Musella, CEO and Cofounder “trimmed the team slightly, focused on efficiency, and now we’re launching a series of products to test new markets. The foundation is strong, now it’s about validating new growth vectors.”

From Retail to Institutional: A Strategic Pivot

Coinrule’s journey began by empowering retail investors to automate strategies once reserved for quants and hedge funds. But as the market evolved, so did the team’s vision.

“We realized the larger opportunity is on the B2B side,” Musella explains.
“Retail traders are great, but the serious capital, the whales, funds, and family offices, need better infrastructure. We’re building for them.”

While consumer-facing automation remains core to the platform, Coinrule’s roadmap increasingly addresses the needs of professional traders,
multi-account management, institutional SLAs, and deeper integration with on-chain execution layers.

Product Expansion and Market Strategy

The team’s latest initiatives include specialized tools targeting high-frequency and algorithmic traders. New micro-products are being launched in parallel, designed to test new verticals across blockchain ecosystems.

Coinrule’s upcoming iOS app marks a strategic milestone. “It’s not just another mobile release,” notes Musella. “The App Store is a powerful acquisition channel, especially since many of our competitors are already there. With our US user base, this can easily drive 10–15% incremental monthly revenue.”

Efficiency Through Engineering

Coinrule’s technical architecture balances speed and scalability. The current app is built as a progressive web app, a deliberate tradeoff to accelerate deployment across Android and iOS.

“use multi-channel frameworks allows us to maintain one codebase,” Musella explains. “Ultimately, we’ll build a fully native version for advanced trading interactions.” Even with a mobile presence, Coinrule’s pro users remain desktop-native.
“These traders log in 10–20 times a day,” Gabriele Musella notes. “Some even keep a second monitor at work just for Coinrule. They treat trading as a craft.”

Serving Funds and Family Offices

Institutional demand for automation is growing fast. For hedge funds, family offices, and proprietary trading desks, Coinrule represents a ready-made quant stack without the overhead of building one internally.

“Why hire a full quant team when you can automate strategies like TWAP, accumulation, or liquidation through Coinrule cloud-based suite?
These tools replicate the precision of institutional execution without the engineering burden.”

The Founder’s Mindset

Reflecting on the decade-long journey, COO and Cofounder Giberstein, credits endurance, not luck, as the defining trait of entrepreneurship.

“If founders knew how hard it would be, they’d never start,” he says with a smile. “A bit of naïveté helps. The most important lesson — one that Y Combinator really drives home — is simple: talk to users and build product. That’s it. Everything else is noise.”

He adds that too many founders “hear what they want to hear” instead of what users actually say — a costly mistake in product development.
“Every wrong build cycle wastes months of human capital. The real reward comes when users stay, evolve with you, and trust you with their strategies.”

Trust as the Ultimate Asset

In a financial platform, user trust is the highest currency.
“We have customers who’ve been with us for years,” Giberstein says.
“They message me directly. Some have my WhatsApp. They’re not just trading — they’re co-building the product with us.”

Coinrule’s SaaS model depends on sustained user engagement, not speculative cycles.
Markets fluctuate, but consistent perceived value keeps subscribers loyal.

“Even in bear markets, users wake up, check their phones, and see fifty trades executed overnight.
That sense of control, of progress, is what keeps them engaged.
We’re not just selling software; we’re providing the experience of being an empowered trader.”

Looking Ahead

As Coinrule transitions into its next chapter, the company sits at a rare intersection: profitable, lean, and ambitious.
The focus is no longer survival, it’s leverage.

“We’ve built a resilient platform, a great team, and a culture that loves building.
The next stage is scaling intelligently, and turning proven engagement into exponential growth.”

Tags: Coinrule, Trading Automation, Fintech, Algorithmic Trading, SaaS, YC Startups, Institutional Trading

The post Building the Future of Trading Automation: Inside Coinrule’s Next Chapter appeared first on Coinrule.