ra. RICARDO AGARContact

TRADING SYSTEMS / RESEARCH / EXECUTION

Algorithm
Engineer.

I’m Ricardo Agar. My work covers systematic trading research, execution software and infrastructure across gold and cryptocurrency markets. At XAU Capital Group, I was the sole Algorithm Engineer, responsible for research, testing, deployment and monitoring.

View work View methodology
GOLD / CRYPTOCURRENCYSelected work
Live systems

Testing, deployment
and production monitoring

XAUUSD + Crypto

Systematic research
and live-market systems

Python / Rust / C#
MQL4 / MQL5

Programming languages

~£800k

Fund scale associated with
algorithm-engineering work

01 / SELECTED WORK

Selected work

Trading research, execution software and operational investigations.

SELECTED INVESTIGATION / LIVE SYSTEMS

XAUUSD broker investigation

I built two monitoring Expert Advisors (EAs) to compare nine brokers after an XAUUSD algorithm performed worse following a broker change.

Brokers investigated
9
Monitoring EAs built
2

Observation

The same strategy and settings produced more losing trades on one broker over the same comparison period.

Comparison

I investigated tick-feed differences, spreads and observed order-execution timing to assess broker suitability for the strategies we were running.

Measurement scope

I treated the MT5 connection-latency reading separately from observed order-execution timing. Feed and execution differences were investigated as possible contributors to the change in results.

SYSTEMATIC TRADING01

Research · Validation · Execution

XAUUSD research
and deployment

At XAU Capital Group, I developed, tested and deployed proprietary XAUUSD trading systems. My responsibilities included forward testing, monitoring and investigating differences between simulated and live behaviour.

DATA ENGINEERING / RESEARCH VALIDATION

COMEX GC and XAUUSD data alignment

I worked on aligning COMEX GC futures L2/L3 data with broker XAUUSD ticks for walk-forward research. The work focused on when information from each feed became available and whether it could be used at a given decision time.

The research included receive-time and feature-availability checks, duplicate and coverage checks, and tests of transaction costs, execution delay and stability across time. Candidates that failed cost or stability checks were rejected or held back from further validation.

Technical details

Research and testing

My work covered mathematical and statistical modelling, backtesting, forward testing and drawdown analysis for XAUUSD strategies. Research included market microstructure, L2/L3 and alternative data, transaction costs, slippage and changing regimes.

Data and evaluation scope

The order-book data came from COMEX gold futures; XAUUSD prices came from broker tick feeds. Research records distinguish discovery runs from independent validation and identify reused evaluation data. The findings described here include research-stage results.

The trading operation was associated with approximately £800k in fund scale.

Python / C# / MQL4 / MQL5

TRADING INFRASTRUCTURE02

Deployment · Monitoring · Platform support

Trading infrastructure
and monitoring

I set up and maintained MT4, MT5 and cTrader environments across Ubuntu and Windows Server, including distributed deployment and real-time performance dashboards.

OPERATIONAL EXPERIENCE / ACROSS SYSTEMS

Operational support across trading systems

I dealt with infrastructure interruptions, execution issues and EA state problems across several trading systems. These included server restarts, provider issues, Windows Server 2022 updates and latency or slippage spikes.

Application issues included stale cached data, duplicate actions, incorrect regime classification and unintended trading lockouts. Unexpected broker orders also occurred during concurrent submissions.

Technical details

Deployment and operation

The work covered distributed VPS/VDS infrastructure, trading-platform configuration, deployment and monitoring.

State and execution boundaries

Relevant checks include cached-input freshness, the EA’s internal state, broker-reported positions and the status of concurrent order requests.

MT4 / MT5 / cTrader / Ubuntu / Windows Server

CRYPTOCURRENCY SYSTEMS03

Observation · Sequencing · Execution

Cryptocurrency
execution software

At AZC, I developed Rust-based software for wallet monitoring and transaction nonce management, supported by distributed VPS/VDS servers.

RESEARCH FINDING / BTCUSDC

BTCUSDC transaction-cost review

Correcting the fee assumptions removed the apparent edge in BTCUSDC perpetual scalping research on Hyperliquid.

Technical details

Execution infrastructure

Work on newly issued tokens included creator-wallet monitoring, transaction sequencing and deployment across internationally distributed servers.

Markets and research

The venture covered newly launched tokens on Pump.fun and separate BTCUSDC perpetual strategy research on Hyperliquid.

Rust / VPS / VDS

SOFTWARE & AI04

Product · Integration · Automation

Zelara: patient software
and API integrations

As Zelara’s founder and software engineer, I built a Python, React and AWS platform covering patient management, scheduling, records and communication.

Technical details

Application development

The platform included calendars, patient records, analytics and financial workflows, with API integrations to third-party platforms.

AI integration

AI voice/chat agents and machine-learning systems supported call handling, triage, appointment booking and outreach within the practice workflows.

Python / React / AWS / APIs

Commercial source code and strategy rules are private. Diagrams show general system structure.

02 / RESEARCH METHODOLOGY

Research
process

A framework for assessing strategies, covering the research and operational checks described here.

Candidates may stop at any stage. The studies above include rejected ideas and work still at the research stage.

Read the broker investigation

Record assumptions
alongside results.

  1. 01

    Question

    Define the market behaviour, instruments and period being investigated.

  2. 02

    Falsifiable hypothesis

    Specify the proposed relationship, its assumptions and the evidence that would contradict it.

  3. 03

    Data validation

    Check timestamps, event order, coverage and source differences against information available at the decision time.

  4. 04

    In-sample research

    Record data, code, assumptions and the variants tested during discovery.

  5. 05

    Out-of-sample / walk-forward

    Use chronological evaluation and record which data informed each decision. Keep independent validation separate from repeated tuning.

  6. 06

    Turnover + realistic costs

    Apply transaction costs to actual turnover and examine spread, slippage and executable prices.

  7. 07

    Robustness + concentration

    Vary cost and execution assumptions. Examine stability across time and whether a narrow period, instrument or market state dominates the result.

  8. 08

    Forward test

    Assess behaviour on new data with the research assumptions fixed.

  9. 09

    Live deployment

    Assess readiness for deployment, including platform checks and monitoring requirements.

  10. 10

    Monitoring + failure analysis

    Compare live behaviour with the simulation and investigate discrepancies. Record uncertainty where the cause remains unresolved.

  11. 11

    Revise or reject

    Record the conclusion and its limits, including rejected strategies and the reasons for stopping.

03 / RESEARCH AND OPERATION

Backtesting
and live trading

Areas considered when reviewing differences between backtests and live behaviour, including the broker, fee and data-alignment investigations above.

RESEARCH ASSUMPTIONSLIVE CONDITIONS
SOURCETIMINGEXECUTIONOBSERVATION
Conceptual illustration of sequencing and delivery differences. No trading results or measured timing.
01Data feedsQuote construction, gaps and broker differences

The broker investigation compared tick-feed behaviour alongside spreads and execution. The COMEX/XAUUSD work also required alignment between distinct instruments and data sources.

02ExecutionOrder timing, slippage and broker behaviour

Execution review covers the order lifecycle, spread and slippage. Connection latency is considered separately from order-execution timing.

03CostsFees, turnover, spread and slippage

Transaction-cost review covers fees, turnover, spread and slippage. In the BTCUSDC scalping research, correcting fee assumptions removed the apparent edge.

04TimingEvent time, availability and ordering

The COMEX/XAUUSD research included receive-time and feature-availability checks to examine whether inputs were available when a decision was made.

05Regime changeVolatility and changing market conditions

Changes in market conditions were a reason to reassess strategies, including changes in daily XAUUSD ATR. Regime-classification errors also occurred in operation.

06ConcentrationDependence on periods and market states

The futures-data research included temporal stability and concentration checks. Candidates were held back or rejected when the evidence did not support further validation.

07DrawdownDepth, duration and exposure

Drawdown analysis formed part of the XAUUSD strategy work. Review considers the sequence of returns, exposure and the duration of adverse periods alongside aggregate results.

08InfrastructureRestarts, cached state and concurrent orders

Operational work across the trading systems included server interruptions, stale EA data, duplicate actions and unexpected broker orders during concurrent submissions.

My main reasons for stopping a strategy were a backtest that no longer held up under review, or a material change in market conditions. That included reassessing XAUUSD strategies as daily ATR changed.

04 / SYSTEM ARCHITECTURE

Trading system
architecture

A general view of the data, research, risk, execution and monitoring layers in an automated trading system.

CONCEPTUAL SYSTEMFIG. 02
01 DATA02 RESEARCH / RISK03 EXECUTION04 MONITORING
Proprietary strategy logic intentionally omitted.
  1. 01 / DATA

    Market / alternative data

    Price, order-flow and alternative inputs, with source and timestamp information retained.

  2. 02 / DATA

    Validation / normalisation

    Timestamp and format alignment, with checks for gaps, delays and inconsistent observations.

  3. 03 / RESEARCH / RISK

    Research / signal layer

    Strategy research and signal calculation using data available at the decision time.

  4. 04 / RESEARCH / RISK

    Risk / position logic

    Exposure constraints and position decisions. Strategy-specific rules are omitted.

  5. 05 / EXECUTION

    Execution layer

    Order submission, acknowledgements and execution records.

  6. 06 / EXECUTION

    Broker / venue

    Broker or venue interfaces, prices, order constraints and execution responses.

  7. 07 / MONITORING

    Monitoring

    Data freshness, process state, positions and execution monitoring.

  8. 08 / MONITORING

    Research feedback

    Operational observations used to review data quality, simulation assumptions and strategy behaviour.

05 / EDUCATION

Engineering
background

I studied Mechanical Engineering with Management at the University of Manchester. The degree covered the mathematical and computational methods I use in quantitative research.

UNIVERSITY OF MANCHESTER / 2025

BEng (Hons) Mechanical Engineering
with Management

01

Probability & inference

Statistics, hypothesis testing, confidence intervals and least-squares methods.

02

Mathematical methods

Calculus, linear algebra, eigenvalues and eigenvectors, differential equations and numerical methods.

03

Computation & measurement

MATLAB and Python modelling, data acquisition and control systems.

06 / EXPERIENCE

Experience

Research, software and engineering.

  1. 2026

    XAU Capital Group

    Algorithm Engineer

    Sole Algorithm Engineer across proprietary trading research, testing, deployment and monitoring.

  2. 2026

    AZC

    Founder & Algorithm Engineer

    Rust-based cryptocurrency infrastructure and systematic BTCUSDC research.

  3. 2025–2026

    Zelara

    Founder & Software Engineer

    AI voice/chat systems, scheduling and patient workflows.

  4. 2025

    University of Manchester

    BEng (Hons) Mechanical Engineering with Management

    Mathematics, statistics, computational modelling and control systems.

  5. 2018

    National Nuclear Laboratory

    Engineering Work Experience

    Engineering work experience placement.

07 / SKILLS

Technical
skills

Quantitative
Mathematical modelling · Statistical analysis · Backtesting · Forward testing · Market microstructure · Execution analysis · Robustness testing
Programming
Python · Rust · C# · MQL4 · MQL5 · React
Infrastructure
Linux / Ubuntu · Windows Server · VPS / VDS · MT4 · MT5 · cTrader · PostgreSQL · Docker · AWS
Engineering computation
MATLAB · Python · Numerical methods · Computational modelling · Data acquisition · Control systems