Tai Tran

Valgo: A Frontier Agentic Trading System for EUR/USD

Mission and Vision of Valgo

Valgo is a fully autonomous trading application that represents the next frontier in agentic trading systems. It combines advanced artificial intelligence with high-performance market analysis to trade the EUR/USD forex pair in real time. The mission of Valgo is to replicate a seasoned trader's decision-making style and execute those decisions consistently, around the clock, with minimal human intervention. This system was developed in collaboration with a domain expert to ensure its behavior aligns with real-world trading practices. In practice, Valgo serves as an AI-powered trader: continuously interpreting market data, making reasoned trading decisions, and managing positions according to a well-defined strategy. By focusing on the highly liquid EUR/USD market, Valgo leverages deep domain-specific insights and data patterns to maintain an edge in execution quality and strategy alignment.

What makes Valgo a “frontier” system is its integration of a Large Language Model (LLM) based agent with a robust trading platform. This means Valgo does not just follow a fixed algorithm; it reasons about the market conditions using a richly informed context, much like a human trader would. The result is an autonomous trading agent that can explain its insights, adapt to evolving scenarios, and make decisions that are consistent with a proven trading style. The vision for Valgo is to close the gap between discretionary trading and automated trading: it aims to deliver the judgment and interpretability of a human expert with the speed and discipline of a machine. 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 (like 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 (for example, 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 (for example, when to trade) are decoupled from technical details (for example, 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 can 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 can be 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 (like an SQLite-backed repository or an in-memory one for testing).
• The architecture is designed with high throughput in mind: the charting engine is GPU-accelerated and the event loop is optimised for real-time updates, ensuring Valgo can handle the fast-paced nature of forex markets without lag or data loss.

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 can be viewed 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 (for example, 1-minute, 5-minute, 1-hour bars) using the data feed adapter logic. This aggregation is efficient and runs in real time, ensuring that the core analysis always has up-to-date candlestick data. In addition to price feeds, Valgo can ingest news and economic event data through a similar adapter mechanism (for example, a news API or economic calendar feed). News events are time-stamped and categorised (for example, macroeconomic report, central bank announcement) so that they can be 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; in practice this repository 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 (for example, 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 might trigger a custom event (for example, NewsEventReceived) which the agent can take 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 can detect 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: If a news feed is connected, Valgo can perform basic sentiment analysis or impact classification. A news adapter could tag incoming news with attributes like sentiment score (positive or negative) and expected impact level (for example, 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 can draw a marker or line at the sweep price level, labelled appropriately (for example, a dashed line indicating a buy-side sweep above a recent high). Break of Structure events might be 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 might show a tooltip with details (type of sweep, price level, time) using a SweepDisplayInfo object. Similarly, an info panel or sidebar might list 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 can reference.

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 trading capability is a Large Language Model agent that has been trained to mimic the decision-making process of a skilled human trader. The training of this agent is what ensures style-aligned execution: the AI does not just trade profitably, it trades in a manner consistent with the philosophies and risk management approach of the domain expert behind it.

Training data collection: Valgo’s development included a significant data collection effort involving real trader input. Over countless trading sessions and historical scenarios, the domain expert labelled data and provided examples of decisions. This included annotations of charts (marking patterns like support/resistance, break of structure points, entries and exits), commentary on why a trade would or would not be taken at a given juncture, and the outcomes of those trades. Additionally, every trade execution and outcome from the expert’s live or simulated trading was recorded. This yielded a dataset of state-action pairs: each entry encapsulates a market state (with all the structured insights Valgo computes, such as trend status, recent patterns, indicator values, and even recent news context) along with the expert’s action (go long, go short, hold, adjust a stop, and so on) and rationale.

LLM fine-tuning: The collected data was used to fine-tune a large language model specifically for trading. Initially, a base LLM with capabilities to reason and understand complex instructions was chosen. It was then trained on the expert-labelled dataset in a supervised manner, effectively learning to map from a description of market context to a recommended trading decision with reasoning.

For example, a training sample might present the LLM with a prompt like:

Market context: EUR/USD is in a downtrend, but a sell-side liquidity sweep just occurred below the recent low at 1.0500, and a bullish BOS followed. The Fed just announced unchanged rates, and volatility is high. Trader’s last action: none (flat). What should the trader do next?

Action: Enter long (buy) with a stop below 1.0480 and target near the next resistance 1.0600. Rationale: The sweep and break of structure suggest a reversal; unchanged rates removed a bearish catalyst, so a relief rally is likely. Risk is defined by the sweep low.

By training on many examples of such scenarios, the LLM learns to produce outputs that include both an action and the reasoning behind the action.

The result of this training is an AI agent that effectively encodes the domain expert’s strategy. It has learned not only what to do in various situations, but why, which is crucial for maintaining alignment with the trader’s style. This style includes factors like preferred trade setups (for example, trading sweeps and BOS signals), risk tolerance (position sizing rules, typical stop-loss distance), and even softer rules like avoiding trades during certain uncertain news events. Because the training data covered a broad range of historical scenarios (bullish trends, bearish trends, volatile news days, quiet markets, and so on), the LLM agent developed a robust understanding of how the expert adapts their approach in each context.

Continuous learning and outcome feedback: Beyond initial training, Valgo is designed to incorporate new data to refine the agent over time. Every autonomous trade the agent executes in simulation or live can be logged along with its outcome (win/loss, drawdown, and so on) and eventually fed back into the training loop. This creates the opportunity for reinforcement learning or fine-tuning with outcome-based adjustments: for instance, if certain patterns of decisions consistently lead to losses, the model can be adjusted to handle those differently in the future. However, any such adjustments are done cautiously to ensure the core style alignment is preserved and the agent does not drift into an unpredictable strategy.

Through this careful training process, the LLM-backed agent becomes, in essence, a digital twin of the human trader’s decision process. It is able to take the rich, structured market insights from Valgo’s data pipeline and analyse them in a manner comparable to an expert human, outputting a decision that is both rationalised and aligned with a proven trading approach.

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 can reason 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 is also given the ability to interact with Valgo’s toolset in a controlled manner. Through an API exposed to the agent, it can perform certain queries or actions as part of its decision reasoning. For instance, the agent could request a fresh calculation of a specific indicator if needed (“Tool: Recalculate Fibonacci levels on 1H chart”) or query the recent price history for a pattern it hypothesises (“Tool: Retrieve last 20 candles to double-check pattern”). In essence, the LLM can call functions provided by Valgo’s core, turning the platform’s analytical capabilities into tools the agent can use on demand. However, in practice the Valgo agent often does not need extensive tool calls because the structured state already includes the most crucial information. The design nonetheless allows for extensibility: if new analysis modules are added (for example, an options flow indicator or a sentiment index), the agent can query those too.

Real-time decision loop

The agent runs in a loop synchronised with market data updates. Typically, upon each significant market event (for example, 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 favor of USD, so a pullback is likely. Risking 0.5% on this trade given moderate confidence.

The reasoning portion can be logged or 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, as seen in the example mentioning change of character and resistance level. This contrasts with opaque black-box trading algorithms. If needed, a user or developer can trace why the AI took a particular trade: the clues are in the structured input and the agent’s logged rationale.

Moreover, since the domain objects are first-class citizens in the system, such as sweeps, BOS events, and trendlines, the agent’s references to them can be directly visualised or cross-checked in the UI. This closes the cognitive loop: the system can explain 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 behavior. 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 can provide, 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 can determine 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 might include 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 is prepared to mark that occurrence. Although CHOCH detection was noted as being experimental in the code, the architecture supports it with the relevant detector in place. 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 can simply refer 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, for example 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, for example a bullish BOS occurring near a known support level while an oversold RSI triggers, this could be packaged into a higher-level insight object as well, which the agent and UI can 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, it is a lot easier to understand “the system went long because it saw a BOS after a liquidity sweep” than understanding a cryptic model feature. Because Valgo can literally say that in its logs or UI, since it has named events for BOS, sweeps, and so on, any action taken by the agent can be traced back to tangible market phenomena.

This is valuable for debugging and for building trust in the system: users and maintainers can audit the system’s behavior in plain market terms. The LLM’s reasoning might read 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, such as multi-pivot sweep analysis, is done efficiently and that results are reused. The architecture uses an AnalysisCacheRepository and caching services so that if the agent or UI needs to query something like “was there a BOS in the last hour?”, it can be answered quickly from stored results rather than recomputing from raw data.

GPU acceleration further means that even computationally intensive indicators, such as calculating ATR or pivot points across millions of data points, can be computed in near real time. This matters when looking at historical data or doing large backtests.

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 not only boosts the system’s analytical capabilities but also enables the LLM agent to reason with concepts that are directly meaningful in trading. The synergy between having these abstractions and an AI that can utilise them results in a highly transparent yet sophisticated decision-making process.

Execution Engine: From Decision to Trade Placement

The ultimate measure of Valgo’s success as an autonomous trading system is its ability to execute trades effectively. Once the LLM agent decides on a trading action, the system’s execution engine takes over to implement that action in the market, or in a simulated environment depending on deployment. Execution in Valgo is designed to be precise, risk-managed, and flexible to accommodate both live trading and simulation.

Trade selection and orchestration

When the agent outputs an action, it typically includes the trade direction (buy or sell), the entry type (market or limit), and any relevant price levels (entry price for limit orders, stop-loss, take-profit). This information is passed to the execution module as a structured order request.

The execution engine first validates the request: it checks that the suggested trade complies with risk parameters and that the market conditions still allow that entry. For instance, if the agent took a few seconds and the market moved drastically, a sanity check might reject a stale order. If valid, the engine proceeds with trade placement.

Valgo’s architecture supports an abstract trading interface, so the same order request can be routed either to a broker API for live execution or to a simulated broker module for backtesting or paper trading. For example, in live mode, an OANDA trading adapter would translate the order into an API call to OANDA’s trade endpoint. In simulation mode, the system instead logs the order in a simulated order book and account balance is adjusted as if the trade were filled.

Risk management

Every trade goes through rigorous risk management checks. The system knows the account’s capital, either real or simulated, and has configurable rules such as maximum percentage of equity to risk per trade and maximum leverage. If the agent’s proposed stop-loss and position size would risk, say, 5% of the account but the limit is 2%, the execution engine will downsize the position to meet the risk limit or may not execute it and will flag an error.

The stop-loss and take-profit are automatically set with the order. For brokers that support OCO orders these are placed together; otherwise, the system will immediately issue stop-loss and limit orders after entry.

The use of an event-driven approach extends here as well: once a trade is executed, events such as TradeOpened, StopLossHit, or TakeProfitHit can be published internally. This allows the rest of the system, including the agent and UI, to react. For instance, if a stop-loss is hit, the agent is informed via the state that the trade closed at a loss, which could influence its next decisions or trigger a learning feedback.

Order execution and monitoring

In live trading, there are nuances like slippage, partial fills, and connection issues. Valgo’s execution module is built to handle these gracefully. It monitors the status of each order after submission. If a market order is used, fills are usually instantaneous, but the system verifies that the entry price is within an acceptable range of the decision price to avoid trading during a spike with huge slippage.

For limit orders, the engine tracks if the order is pending, and cancels or modifies it if needed. For example, if after some time the rationale for the trade no longer holds, the agent could signal to cancel a pending order. In simulation mode, the engine simulates these behaviors: it can model slippage or simply execute at the next tick’s price to mimic real conditions. The simulation mode also records detailed logs of trade progression, which is useful for analysing performance in backtests.

Feedback to the agent and UI

Once trades are executed, the system updates the UI to reflect positions and P/L in real time. A dashboard might show the current open trades, entry price, current price, unrealised profit, and so on, updating with each tick. The LLM agent’s state input for the next decision cycle will also include current position information.

This feedback loop is important: the agent needs to know it is in a trade, as that might change its behavior. For example, if it is already long, it might not take another long in the same direction unless it is adding to the position as per strategy. Moreover, the outcome of trades, win or loss, is recorded and can be used as described for ongoing learning.

The closed trade data, such as entry, exit, profit, and duration, can be aggregated to produce performance metrics, which can be reviewed to understand behaviour over time or used to further calibrate the system.

Simulation vs live deployment

Valgo was developed and tested extensively in simulation before live deployment. In simulation mode, every aspect of the trade execution pipeline can be tested against historical data or in a sandbox environment:

• The agent reads historical data tick by tick as if it were live, makes decisions, and the simulated execution executes them. This process can be accelerated to run backtests over months of data in a short time.
• It provides a wealth of performance statistics, such as win rate, Sharpe ratio, and maximum drawdown, that validate the strategy.

The benefit of Valgo’s unified architecture is that the same logic is used in simulation and live trading; only the adapter at the final step differs. This means confidence built in backtests and paper trading carries over to live usage. When switched to live mode, the system uses real-time data feed and real broker execution. Thanks to the clean separation, this is as simple as changing configuration to point to the live OANDA account and enabling the trading adapter with API credentials. The entire pipeline from data ingestion, analysis, and agent decision to execution works in the live market as it did in tests.

Order management and strategy execution

Valgo’s execution engine is not just fire-and-forget; it is capable of managing complex order strategies if needed. For example, if the trading style includes scaling in or out of positions, the agent could output a plan like “enter half position now and half if price improves by X”. The execution engine can handle such multi-part orders, scheduling or conditionally sending the second entry.

It can also manage trailing stops, either by an algorithm, for example moving stop to breakeven after 20 pips gain, or by obeying commands from the agent, which might decide to tighten the stop if a certain market condition is met. This gives Valgo a level of sophistication akin to an experienced trader actively managing trades.

Overall, the execution component of Valgo ensures that once a trade decision is made, it is carried out swiftly and safely. With robust risk checks, flexible connectivity to real or simulated brokers, and constant feedback loops, Valgo’s trade execution closes the autonomous trading loop. The agent’s strategies are not just theoretical decisions; they directly influence market positions, and the outcomes of those positions feed back into the system’s knowledge. This tightly integrated cycle from decision to execution to outcome is what allows Valgo to operate as a true autonomous trading agent.

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:

• Integration of AI reasoning in trading: 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 can interpret and reason about market conditions in real time, which is a capability traditionally limited to human traders. The agent can articulate why it is taking actions, which is rare in automated trading. This not only provides clarity for oversight but also means the strategy can be more nuanced and adaptive than a hard-coded algorithm, staying aligned with a human-like understanding of the market.
• 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 potentially more sustainable, as it avoids actions that a human would consider reckless even if a backtest might find them profitable in the short run. 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 scripts or monolithic applications that can be brittle. In contrast, Valgo has industrial-grade engineering: a scalable design that can handle high data loads and be 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 means Valgo can evolve: today it is focused on EUR/USD, but the core could be 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. It also means Valgo can be used as a discretionary trading tool or for analysis alone, if desired, by turning off the 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 wisdom 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 feedback loop, from data to insight to action and back to new data, is tightly integrated, which enables continuous improvement and adaptation and makes the system easier to understand and trust.

This makes Valgo more than just another trading bot; it is an autonomous trading partner at the frontier of technology and finance, positioned to navigate the complexities of the EUR/USD market with both machine precision and human-like insight.