Tai Tran

Valgo: An Agentic Trading System for EUR/USD

Mission and Vision of Valgo

Valgo is an autonomous trading application exploring the frontier integration of Large Language Model (LLM) agents with high-performance market analysis. It is designed to trade the EUR/USD forex pair by approximating a seasoned trader's decision-making style. The mission is to approximate discretionary judgment in a system that can execute consistent decisions with minimal human intervention. This system was developed in collaboration with a domain expert to align its behaviour with observed trading practices. In operation, Valgo serves as an AI-powered trader: continuously interpreting market data, making reasoned trading decisions, and managing positions according to a defined strategy.

Scope & Status

• Asset: Single-pair focus (EUR/USD).
• Mode: Research system, currently operating in high-fidelity simulation.
• Key Contributions: Production-grade architecture, LLM integration with structured state, and first-class SMC abstractions.
• Performance: No published out-of-sample live trading results yet.

What differentiates Valgo is its integration of an LLM-based agent with a robust trading platform. Valgo does not just follow a fixed algorithm; it reasons about the market conditions using a richly informed context. The result is an autonomous agent that explains its insights and aims to adapt to evolving scenarios consistent with the domain expert's style. This document provides an overview of how Valgo’s architecture and components come together to achieve that mission.

Architecture Overview: Modular, Clean, and Event-Driven

At its core, Valgo is built on a modular Clean Architecture that enforces a separation of concerns across different layers of the system. This design ensures that the trading logic, user interface, data handling, and external integrations remain loosely coupled yet highly coordinated. The major layers of Valgo include:

• Core (Domain & Application): Encapsulates all business logic, trading rules, and analysis algorithms. This is framework-agnostic code that defines how the trading strategy works and how data is processed. The domain sub-layer contains entities, value objects, and services (e.g. market analysis algorithms), while the application sub-layer includes use cases, orchestrators, and command handlers that implement higher-level workflows.
• Adapters (Infrastructure): Connect the core to external systems and libraries. Adapters handle data feed integration, database persistence, logging, and external APIs (e.g. the OANDA market data API). By isolating these, Valgo can swap out data sources or broker APIs without altering core logic.
• GUI (Presentation): The user interface layer is built with PySide6/Qt for interactivity and VisPy/OpenGL for high-performance charting. It includes chart widgets, view models, and controllers (following an MVVM pattern) to present real-time market data, technical indicators, and the agent's actions to the user. The GUI remains separate from the core logic but subscribes to events and state changes to update visuals.
• Shared (Cross-cutting): Utilities and services used across all layers, such as the dependency injection container, configuration management, and common utilities, reside here.

This clean segregation follows Domain-Driven Design (DDD) principles and ensures that business decisions (e.g. when to trade) are decoupled from technical details (e.g. how to draw a chart or fetch data). Communication between layers primarily happens through an event-driven design: Valgo implements an internal Event Bus for pub/sub messaging and uses a Command/Query Mediator for request-response interactions. For example, when new market data arrives, a domain event is published; the UI layer’s chart controller subscribes to these events to update the display, and the agent module subscribes to consider the new data for decision-making. This event-driven approach improves responsiveness and decoupling: components react to the data flow asynchronously without tight coupling.

Another key aspect is the use of dependency injection and inversion of control via a service container. This allows the LLM agent, analytics services, and other components to be wired together flexibly at runtime. For instance, the agent is provided with interfaces to query data or execute trades without needing to know the concrete implementations, which makes it easy to switch from simulated trading to a live broker adapter by changing configuration.

Architectural patterns in summary:

• The system employs CQRS (Command Query Responsibility Segregation) where read operations (queries) and write operations (commands) are handled separately for clarity and scalability.
• It uses a Repository pattern for data access, meaning the core logic speaks to abstract repositories (for market data, account state, and so on) and the adapter layer provides concrete implementations (e.g. an SQLite-backed repository or an in-memory one for testing).
• The architecture is built for high throughput: the charting engine is GPU-accelerated and the event loop is optimised for real-time updates, ensuring Valgo handles the fast-paced nature of forex markets without lag or data loss.

Deployment Model and Scaling Capabilities

Valgo is deployed as a high-performance desktop application: the PySide6/Qt UI, VisPy/OpenGL charting engine, analysis services, repositories, and LLM-driven agent run in a unified process, communicating directly with OANDA and local SQLite/Parquet storage. This design minimises latency and simplifies operations for both live trading and intensive simulation cycles. The architecture, however, is intentionally structured so that core services can be promoted to a separate backend process or host as compute demands increase. This design enables moving the heaviest analysis and agent loops onto a dedicated machine, keeping the desktop application as a thin, real-time client that communicates with that backend over a network boundary, without changing the core domain or application logic.

Data Pipeline: From Raw Data to Actionable Insights

Valgo operates on a continuous stream of market information, transforming raw inputs like price ticks and news events into structured, actionable insights. The data pipeline runs as a sequence of stages:

1. Data Ingestion: Valgo connects to external data sources through its adapter layer. For price data, it streams live candlestick data from OANDA’s API, focusing on the EUR/USD pair. The system aggregates tick-by-tick prices into OHLC candles at configurable timeframes (e.g. 1-minute, 5-minute, 1-hour bars). Aggregation runs in real time so analysis always sees up-to-date candles. In addition to price feeds, Valgo ingests news and economic event data through a similar adapter mechanism. News events are time-stamped and categorised (e.g. macroeconomic report, central bank announcement) so that they are correlated with market moves.
2. Data Storage and Access: All incoming raw data is stored in an on-disk database (SQLite in WAL mode) for reliability and historical reference. The design uses repository abstractions so that the core logic queries a MarketDataRepository interface; the repository intelligently fetches from the in-memory cache or database as needed. This allows Valgo to seamlessly access historical data for analysis or training without changing its core code. Additionally, Valgo includes utilities to download and persist larger historical datasets (e.g. multi-year price history) in efficient formats like Parquet for offline analysis. Maintaining a local cache of historical EUR/USD data not only speeds up analysis (no repeated network calls) but also provides a rich dataset for the LLM agent’s training and backtesting.
3. Real-Time Data Stream Processing: As new candles form or news arrives, Valgo’s event-driven core broadcasts events to notify interested components. For instance, when a new 5-minute candle closes, a MarketDataUpdated event (or similar) is published. The analysis services subscribe to these events to update technical indicators or detect emerging patterns. Similarly, a significant news event triggers a custom event (e.g. NewsEventReceived) which the agent takes into account. The use of events and asynchronous processing means that even under high-frequency updates, the system remains responsive: GUI updates and agent decisions happen in parallel without blocking each other.
4. Analytics and Insight Generation: This is where raw data turns into actionable insight. Valgo’s core includes an Analysis Services module dedicated to technical analysis computations. These services take in fresh market data and produce higher-level information:
   • Market Structure Analysis: Valgo continuously analyses market structure, identifying swing highs and lows, trends, and potential breakpoints. For example, it has a MarketStructureAnalyzer and associated detectors that watch for Break of Structure (BOS) events or trend changes. A BOS event in trading indicates that the market has broken a previous significant high or low, signalling a possible trend reversal or strong momentum.
   • Liquidity Sweep Detection: In line with Smart Money Concepts (SMC) methodology, Valgo detects liquidity sweeps: sudden wicks that grab liquidity beyond a recent high or low and then reverse (often termed buy-side or sell-side sweeps). The analysis service orchestrates multi-stage detection, first identifying basic sweeps (BSS/SSS), then higher-order patterns like BOS and CHOCH (Change of Character) in sequence. These patterns are critical insights that a human trader might note as potential reversal signals or traps set by large institutions; Valgo identifies them algorithmically in real time.
   • Technical Indicators: The system computes common technical indicators (moving averages, ATR, RSI, and so on) on the fly as needed. Indicators are implemented as part of the analysis pipeline and their values are updated with each new candle. For example, an ATR (Average True Range) might be used to gauge volatility and adjust the agent’s risk or detect an unusually quiet market.
   • News Sentiment Analysis: When a news feed is connected, Valgo performs basic sentiment analysis and impact classification. The news adapter tags incoming news with attributes like sentiment score (positive or negative) and expected impact level (e.g. low/medium/high impact economic news). These become part of the insight pipeline, so the agent knows not just that news happened, but whether it is likely good or bad for the Euro or USD and how significant it is.
5. UI Tooling and Visualisation: All these insights are made visible and actionable through Valgo’s integrated analytics tooling in the user interface. The GUI overlays technical analysis findings on the live chart for transparency. For instance, when a liquidity sweep is detected, the chart draws a marker or line at the sweep price level, labelled appropriately (e.g. a dashed line indicating a buy-side sweep above a recent high). Break of Structure events are annotated on the chart as well, giving visual confirmation of a trend regime change. Valgo’s UI controllers coordinate with the analysis events to display this information contextually: hovering over a sweep line shows a tooltip with details (type of sweep, price level, time) using a SweepDisplayInfo object. Similarly, an info panel or sidebar lists recent major events such as “BOS detected at 1.0745 (10:30 GMT)” or “ECB news: Rate hike expected – High impact”. This rich visualisation capability ensures that the insights derived from raw data are presented in a human-intuitive way, both for any user monitoring the system and for the trace logs that the agent references.

By the end of this pipeline, Valgo has transformed raw streaming data into structured market intelligence. Instead of just seeing a time series of prices, the system recognises and surfaces key market events, technical signals, and context from news. These become the inputs, or “tools”, for the next stage: the LLM-based trading agent that will decide how to act on these insights.

LLM-Backed Agent Training with Real Trader Data

At the heart of Valgo’s autonomous capability is a Large Language Model agent trained to approximate the decision-making process of a skilled human trader.

Training data collection: Valgo’s development leveraged a dataset of state-action pairs collected from a domain expert. This dataset includes market states (structured insights like trend status, indicator values, and news context) paired with the expert’s specific action (e.g., "Long", "Short", "Hold") and their rationale.

LLM fine-tuning: A base LLM was supervised fine-tuned (SFT) on this dataset to map market contexts to trading decisions. For example, given a prompt describing a "sell-side liquidity sweep followed by a bullish break of structure," the model learns to output a "Long" action with a corresponding rationale, citing the reversal pattern. This process aims to capture the expert's style, including their preferred setups and risk tolerance.

Continuous learning: Beyond initial training, Valgo logs every autonomous trade and its outcome. These logs are used for offline periodic fine-tuning, allowing the model to be updated with new experience and evaluated against held-out data. These updates are run offline on batches of new data and re-evaluated against a fixed held-out set before deployment. This approach helps refine the agent's behaviour over time while attempting to avoid the drift often seen in online reinforcement learning systems.

The goal is for the agent to behave like a "digital twin"—analysing structured market insights and outputting rationalised decisions that reflect the domain expert’s established patterns.

Agentic Integration: LLM Decision-Making with Tools and Data

Having an LLM that understands the trading style is one thing; integrating it into a live trading system in an agentic role is another. In Valgo, the LLM agent is deeply integrated into the platform, effectively serving as the brain that drives trading decisions. The agent operates within the system as follows.

Structured state input

Rather than feeding the LLM raw time-series data, which would be both impractical and not meaningful to a language model, Valgo provides the agent with a curated, structured representation of the current market state. This state includes key elements such as:

• Recent price action and technical state: For example, “EUR/USD is currently 1.0530, in an uptrend on the 1H timeframe but ranging on the 5M. The 50-period moving average is sloping up. A buy-side sweep was detected at 1.0525 ten minutes ago, followed by a break of structure above 1.0540. Current volatility (ATR) is moderate.”
• Active market structure and indicator values: For example, “Nearest support at 1.0500, nearest resistance at 1.0575. RSI is 68 (near overbought). Last BOS: bullish at 10:30. Trend momentum: medium.”
• News context: For example, “Latest news: ECB press conference ongoing (neutral tone), US unemployment data better than expected (bullish USD). Sentiment: mixed.”
• Current positions and orders (if any): For example, “No open positions. Last trade closed +20 pips profit. Available capital $X, risk per trade 1%.”

By structuring the input this way, Valgo ensures the LLM has all relevant information at its fingertips in symbolic form, similar to how a human trader might summarise the state before making a decision. The use of value objects and domain abstractions (like SweepDisplayInfo or trend objects) in the code means these summaries are generated from well-defined data structures rather than ad-hoc calculations. This not only reduces noise but also makes the agent’s job easier: it reasons about concepts like "trend" or "support level" directly instead of trying to infer them from raw numbers.

Tool use and queries

Although the state input is comprehensive, the agent also interacts with Valgo’s toolset in a controlled manner. Through an API exposed to the agent, it performs targeted queries or actions as part of its decision reasoning. For instance, the agent requests fresh calculations of specific indicators when needed (“Tool: Recalculate Fibonacci levels on 1H chart”) or queries recent price history for patterns it hypothesises (“Tool: Retrieve last 20 candles to double-check pattern”). In essence, the LLM calls functions provided by Valgo’s core, turning the platform’s analytical capabilities into tools the agent uses on demand. The Valgo agent does not need extensive tool calls because the structured state already includes the most crucial information, but the design remains extensible: when new analysis modules are added (e.g. an options flow indicator or a sentiment index), the agent queries those as well.

Real-time decision loop

The agent runs in a loop synchronised with market data updates. Typically, upon each significant market event (e.g. a candle close or a high-impact news release), the system triggers the agent to make an assessment. The LLM processes the current state input and any relevant recent context, including optionally its own last decision and outcome, and then generates an output. The output from the LLM is structured either via a predefined format or prompt discipline so that it contains a clear action recommendation and optionally a rationale.

For example, the output might be:

Action: Enter SHORT at market 1.0530, Stop 1.0560, Take Profit 1.0480.

Rationale: A bearish change of character was identified after the bullish run-up; price failed to break the higher resistance and economic data turned in favour of USD, so a pullback is likely. Risking 0.5% on this trade given moderate confidence.

The reasoning portion is logged and optionally displayed in a console for transparency, but the crucial part is the action.

Interpretability and reasoning

One of Valgo’s design priorities is that any autonomous action should be interpretable. Because the agent makes decisions using the same terms a human trader would, it inherently provides a form of explanation. Key market structure objects and indicator readings that led to the decision are part of the agent’s reasoning chain. This contrasts with opaque black-box trading algorithms. Because the same BOS/sweep objects are visualised in the UI, the agent’s rationale can be traced directly to on-chart annotations. This closes the cognitive loop: the system explains itself in terms of its own visualised analysis, which builds trust with human operators and aids debugging.

Operating constraints

To ensure safety and consistency, the agent’s operation is constrained by a set of rules and guardrails within the system. For example, the agent might be prevented from making more than a certain number of trades per time window, or from taking trades outside of designated trading hours if the style dictates avoiding low-liquidity periods. It also respects risk constraints set at the system level: even if the LLM were to output an overly aggressive position size, the execution module will cap the risk to predefined limits.

These measures ensure that the autonomous agent remains within the bounds of prudent trading and cannot, due to error or odd output, deviate from acceptable behaviour. Essentially, the LLM agent has freedom within the box defined by the trading strategy and risk management parameters.

Through this tightly integrated setup, Valgo's LLM agent acts as a knowledgeable trader operating inside a powerful trading platform. It leverages all the analysis and data that Valgo provides, makes a decision, and communicates that decision in a structured way. This agentic loop runs continuously, enabling Valgo to operate autonomously: scanning the market, understanding it, and acting on it, all in real time.

Market Structure and Indicators: First-Class Abstractions for Reasoning

One of the distinguishing features of Valgo is the way it treats market structure and technical indicators as first-class abstractions within the system. This design choice is pivotal for both the performance of the analytics and the interpretability of the AI agent's decisions. In traditional algorithmic trading, raw price data might be fed into a machine learning model which then produces predictions or signals without a human-understandable explanation. Valgo, in contrast, elevates the key elements of technical analysis to core data structures and concepts, which benefits the system in several ways.

Market structure objects

The concept of market structure refers to the pattern of highs, lows, trends, and consolidations that price forms over time. Valgo explicitly models these. For instance, it identifies swing highs and swing lows and determines the current market trend (bullish, bearish, or ranging) based on a hierarchy of timeframes. When a Break of Structure (BOS) occurs, for example when the price makes a higher high above a previous swing high, Valgo generates an event or record of that occurrence. This BOS object includes information like the price level of the break, the time it occurred, and which prior structure was broken.

Likewise, for a Change of Character (CHOCH), a term in SMC for when a trending market shows the first sign of reversing trend, the system marks that occurrence via a dedicated detector that is already wired into the analysis pipeline and being continuously tuned against historical data. All these structural events are stored in memory, and optionally persisted, as part of the market’s state.

Indicators as data feeds

Traditional indicators like moving averages, oscillators, and volatility measures are continuously computed by Valgo’s analysis services. Each indicator value is treated as part of the state rather than something ephemeral. For example, a 50-period moving average on the 1-hour chart is computed whenever new data comes in and is accessible as a property in the market model. If multiple timeframes or multiple instruments were in use, the system would manage separate instances for each combination.

Because these indicators are first-class, the agent does not need to calculate them from scratch or guess their value: it simply refers to the current 50 SMA or current RSI from the structured input it receives. This not only saves computational effort but also ensures consistency; the values the agent sees are exactly the values the UI displays and the strategy logic uses.

Composite signals and patterns

Beyond basic indicators, Valgo defines higher-order composite signals as needed. An example is the liquidity sweep detection mentioned earlier. When the sweep detection service identifies a sweep, it creates a structured object, e.g. a LiquiditySweep object, containing details such as type (buy-side or sell-side sweep), the price level of the sweep, time, and maybe the size or length of the wick.

Similarly, if Valgo identifies a confluence of signals, e.g. a bullish BOS occurring near a known support level while an oversold RSI triggers, it packages this into a higher-level insight object as well, which the agent and UI label as a special scenario. The important aspect is that Valgo does not throw away these insights or hide them inside a "black box" model; it retains them as explicit data structures. This leads to a form of built-in documentation of what the system is observing in the market at any time.

Interpretability and debugging

Treating these elements as first-class citizens yields immediate interpretability. For a human operator, it is significantly easier to understand “the system went long because it saw a BOS after a liquidity sweep” than understanding a cryptic model feature. Because Valgo logs and visualises those events in its UI, with named events for BOS, sweeps, and so on, any action taken by the agent is traced back to tangible market phenomena.

This is valuable for debugging and for building trust in the system: users and maintainers audit the system’s behaviour in plain market terms. The LLM’s reasoning reads like a trader’s journal entry, referencing these objects, which builds confidence that the AI is “seeing” the market in a human-relatable way.

Efficiency and performance

There is also a practical performance angle. By computing and caching these structures, with the help of GPU acceleration for heavy computations, Valgo ensures that complex pattern detection is done efficiently. The architecture uses an AnalysisCacheRepository so that if the agent or UI needs to query something like “was there a BOS in the last hour?”, it is answered quickly from stored results.

GPU acceleration further means that even computationally intensive indicators are computed in near real time. Performance profiling on RTX-class hardware demonstrates rendering of ~10,000 candlesticks at >160fps and market structure analysis completion in ~30–40ms, well within typical latency requirements for real-time EUR/USD decision loops (minutes-level bars).

In summary, Valgo’s approach of having market structure elements and indicators as first-class abstractions creates a shared language between the system and the trader, or AI agent. It turns the tacit knowledge of technical analysis into explicit, code-level objects. This enables the LLM agent to reason with concepts that are directly meaningful in trading. The synergy between having these abstractions and an AI that utilises them results in a highly transparent yet sophisticated decision-making process.

Execution Engine: Unified Simulation and Live Trading

The execution engine translates agent decisions into market orders, handling validation, risk management, and routing. A key architectural strength is the unified execution path: the same core logic drives both simulation and live trading, with only the final adapter differing (e.g., OandaAdapter vs SimulatedBroker).

Risk and Orchestration

Before any order is placed, the engine validates it against configurable risk rules (e.g., max leverage, max equity risk per trade). It handles order lifecycle events—placement, fills, stop-loss triggers—via the internal event bus, ensuring the agent and UI are immediately updated.

Dual-Mode Readiness

Valgo is engineered to support both research and production:

• High-Fidelity Simulation: The agent processes historical data tick-by-tick, with the simulation engine modelling latency and slippage. This allows for "time-travel" backtesting that exercises the full system stack.
• Live Execution: The system is integrated with OANDA’s API for live execution. However, current operations are restricted to simulation and paper trading to validate performance before enabling real capital deployment.

This design ensures that the strategy validated in simulation is the exact same code that runs in production, modulo configuration (live OANDA adapter vs simulated broker), eliminating the common "backtest-to-live" implementation gap.

Validation Roadmap and Technical Challenges

While Valgo demonstrates a production-grade architecture and a novel approach to agentic integration, it is important to distinguish between the maturity of the software engineering and the validation of the trading strategy. The system currently serves as a powerful "concept piece" for how LLMs can be integrated into a finance loop, but several technical and quantitative challenges remain to move from a research prototype to a candidate alpha generator.

Quantitative Performance and Overfitting Risks

The most immediate hurdle is rigorous performance verification. Although the simulation engine produces metrics like win rate and Sharpe ratio, these must be validated on large-scale out-of-sample datasets.

• Backtesting Plan: We are currently configuring a split-sample test: training on 2015–2020, validating on 2021–2022, and testing on 2023–2024 data. This will be conducted with realistic OANDA spread and transaction cost models.
• "Digital Twin" Overfitting: There is a risk that the LLM merely memorises past actions. To address this, we plan to benchmark the LLM agent against simpler baselines—such as a static rule-based engine and a gradient boosting model—using the same structured inputs. This will isolate the incremental value, if any, of the LLM's reasoning capabilities.

Latency and Real-Time Constraints

Deploying an LLM in a real-time trading loop introduces latency challenges that do not exist in standard algorithmic trading.

• Inference Latency: The time taken for the LLM to ingest the state, generate tokens, and parse the output (the "market data to order" loop) must be strictly characterised. While acceptable for 1-hour candles, this latency may be prohibitive for 1-minute or 5-minute scalping strategies.
• Robustness: The system must handle non-deterministic failure modes, such as LLM timeouts, hallucinations, or malformed JSON outputs, without entering undefined states.

Validating Smart Money Concepts (SMC)

Valgo relies heavily on "Smart Money Concepts" (SMC) like Break of Structure (BOS) and Liquidity Sweeps as first-class signal sources. While these align with the domain expert's discretionary style, they require statistical validation to prove they hold positive expectancy on EUR/USD across different market regimes. We aim to conduct sensitivity analyses on the definitions of these patterns (e.g. swing depth, wick ratios) to ensure the underlying signals are robust and not just visual artifacts.

Closing the Loop: From Data to Insight to Autonomous Execution

Valgo brings together all the components discussed above into a unified platform that closes the loop in the trading process. It stands out among trading systems by handling the entire workflow, from raw data ingestion to insightful analysis to decision-making and finally to trade execution, with minimal human input. This end-to-end autonomy is built on a foundation of transparency, adaptability, and expert knowledge integration.

In comparison to existing platforms, Valgo is distinct in several respects:

• Real-time AI reasoning: While algorithmic trading platforms and expert advisors have existed for years, they typically rely on fixed rules or opaque machine learning models. Valgo’s use of an LLM-based agent means it interprets and reasons about market conditions in real time, which is a capability traditionally limited to human traders. The agent articulates why it is taking actions, which is rare in automated trading. This not only provides clarity for oversight but also opens the door to strategies that are more nuanced and adaptive than a purely hard-coded algorithm, while remaining inspectable.
• Style alignment and human collaboration: Many trading bots optimise purely for profit and might churn through thousands of trades in a way no human would, often exploiting minute inefficiencies. Valgo instead is trained to a particular style: it behaves more like a professional trader than a high-frequency robot. This makes its trading more interpretable and keeps its behaviour closer to what a human would consider acceptable, rather than pure backtest-driven optimisation. The close collaboration with a domain expert imbued Valgo with deep insights, such as the importance of market structure and context, that set it apart from generic automated strategies.
• Architectural robustness: Valgo’s clean, event-driven architecture gives it a level of reliability and modularity that is crucial for mission-critical trading operations. Many trader-developed bots or tools are simple scripts or brittle monolithic applications. In contrast, Valgo has a production-grade architecture: a scalable design that handles high data loads and is routinely extended over time. For instance, adding a new data feed or a new analysis module is straightforward due to the adapters and service-oriented structure. This modularity allows Valgo to evolve: today it is focused on EUR/USD, but the core is already extended to other instruments or markets by plugging in new modules without rewriting the whole system.
• High-performance visualisation and UI: Unlike many automated trading systems that run headless or only output signals, Valgo includes a full-fledged UI with GPU-accelerated charting. This is more than cosmetic; it allows traders or developers to observe the same information the agent sees, in real time. The UI acts as a window into the agent’s mind, showing the candlesticks, indicators, and annotated patterns. Valgo is also used as a discretionary trading tool or for analysis alone, when desired, by turning off autonomous execution. This dual use, both as a manual toolkit and an automated system, makes it useful to a broader range of technically minded users.

In closing, Valgo represents a synthesis of human trading heuristics and modern AI automation. It takes in market data, distils knowledge through advanced analytics, reasons on that knowledge with an AI agent, and translates decisions into executed trades all within a singular, coherent platform. The tight integration of data, insight, and action creates a feedback loop that supports continuous research and refinement.

This makes Valgo more than just a trading bot; it is an autonomous research platform positioned to explore the complexities of the EUR/USD market using agentic AI.