Mauricio Vellasquez
- AI Manager and programmer 在 Chief AI Officer
- 巴西
- 2449
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构建算法交易的未来。
作为数据科学家,我专注于创建由机器学习驱动的高性能交易解决方案。Ratio X不仅仅是一个工具;它是一个完整的生态系统,旨在使您的交易专业化。
加入数千名已经用"概率"取代"预测"的交易者行列。
🚀 加入革命:
Ratio X Toolbox on MyFxbook:
🌐 完整武器库(包含所有EA):获取整个Ratio X套件的终身许可证: https://ratioxtrade.com
🔓 解锁代码(Ratio X DNA):面向开发者和企业家:获取源代码(.mq5)并建立您自己的业务: https://ratioxtrade.com/dna
📈 每日AI分析(免费): https://ratioxtrade.com/signals
📞 联系我(Telegram): https://t.me/ratioxtrading
🤝 联盟计划(赚取60%): https://app-vlc.hotmart.com/affiliate-recruiting/view/7908R101260853
作为数据科学家,我专注于创建由机器学习驱动的高性能交易解决方案。Ratio X不仅仅是一个工具;它是一个完整的生态系统,旨在使您的交易专业化。
加入数千名已经用"概率"取代"预测"的交易者行列。
🚀 加入革命:
Ratio X Toolbox on MyFxbook:
🌐 完整武器库(包含所有EA):获取整个Ratio X套件的终身许可证: https://ratioxtrade.com
🔓 解锁代码(Ratio X DNA):面向开发者和企业家:获取源代码(.mq5)并建立您自己的业务: https://ratioxtrade.com/dna
📈 每日AI分析(免费): https://ratioxtrade.com/signals
📞 联系我(Telegram): https://t.me/ratioxtrading
🤝 联盟计划(赚取60%): https://app-vlc.hotmart.com/affiliate-recruiting/view/7908R101260853
Mauricio Vellasquez
I’m thinking about creating an Expert Advisor with integrated AI, but the API costs will be mine.
You just attach it to the chart, set up your configuration, and voilà, the AI does the rest of the work. How much do you think this would be worth?
You just attach it to the chart, set up your configuration, and voilà, the AI does the rest of the work. How much do you think this would be worth?
Mauricio Vellasquez
已发布产品
Ratio X AI Gold Fury - 基于人工智能的XAUUSD交易专家顾问 一个将专业技术分析与DeepSeek人工智能相结合的复杂交易系统,旨在最大化黄金市场的交易机会。 本EA的独特之处 与传统的具有刚性硬编码规则的专家顾问不同,Ratio X AI Gold Fury使用人工智能来分析市场环境。然而,它不会盲目遵循AI的建议。相反,它需要同时获得AI批准和风险管理验证后才会执行任何交易。 核心功能 AI推荐止损/止盈: 让AI根据当前市场条件确定最佳止损和止盈水平 追踪止损系统: 当交易朝有利方向发展时自动锁定利润 自动手数计算: 基于账户余额百分比的风险计算 魔术数字支持: 同时运行多个EA实例 通用品种支持: 适用于任何经纪商的品种命名(XAUUSD、xauusd+、GOLD等) 双重操作模式: AI驱动交易或纯技术分析 工作原理 实盘模式 - AI辅助交易 由DeepSeek API驱动进行情境市场分析: 收集市场数据并计算技术指标(RSI、MACD、EMA、布林带) 打包数据并发送至DeepSeek AI进行分析
Mauricio Vellasquez
已发布产品
Ratio X Breakout EA — 精确突破执行 重要提示: 购买后可在评论区获取完整配置指南。 概述 Ratio X Breakout EA是一个自主交易系统,旨在捕捉由蜡烛突破触发的方向性走势。其逻辑核心是用户定义的参考蜡烛,建立框架,当价格行为果断突破该结构时,EA进入市场。 核心方法论 系统的核心是参考蜡烛(可配置时间框架和时间,例如13:30的M5)。EA标记其高点和低点。第一根随后的蜡烛主体收盘价超出此范围即定义方向偏向: 收盘价高于高点:仅做多偏向 收盘价低于低点:仅做空偏向 一旦方向确认,EA在突破水平执行限价入场,止损设在参考蜡烛的另一侧。用户可以定义缓冲区(以点数计)来控制入场和保护水平。 交易管理 止损和止盈 可通过固定点数距离或风险回报倍数配置 用户定义的入场和出场水平缓冲区 追踪选项 基于步进R倍数的追踪 使用蜡烛高低点的动态追踪 可配置的追踪参数 执行纪律 默认每天最多三笔交易 测试模式可用于MQL5市场验证(测试目的忽略所有过滤器) 参考蜡烛确认后每天一个方向偏向 主要特点 基于突破的架构,具有透明、规则驱动的执行
Mauricio Vellasquez
Integrating AI Decision-Making in MetaTrader 5: Technical Summary & Risk Considerations
Overview
Adding AI to MT5 Expert Advisors (EAs) enables more contextual, multi-signal decisions, but increases engineering complexity, cost, and governance needs.
Architecture
API integration: The EA sends market snapshots to cloud models through MT5’s WebRequest. Users must explicitly allow outbound calls and allowlist the service URL (e.g., api.openai.com).
Data model: Build a structured payload that aggregates multiple timeframes (M5/M15–M30/H1–H4/D1–W1) and key indicators (RSI, short/long EMAs, MACD, ATR, volatility, trend direction).
Multi-timeframe logic:
Short term: noise filtering and entries.
Intraday: pattern recognition.
Medium term: trend confirmation.
Long term: regime context.
This depth adds nuance but raises data and compute demands.
Regime detection & adaptation
States: trending, range-bound, high volatility, crisis.
Signals: autocorrelation and volatility stats for classification.
Position sizing: combine Kelly-style fractions (win rate/payoff) with volatility-scaled exposure to throttle risk in unstable periods.
Risk architecture
Layered controls: circuit breakers, max drawdown caps, VaR monitoring, correlation limits, daily loss limits.
Dynamic risk: adjust parameters in real time based on market state and system P&L.
Metrics: live Sharpe, Calmar, Sortino, and Expected Shortfall for risk-adjusted tracking.
Implementation challenges
Latency: API round-trips ~200–2000 ms plus model compute can cause slippage.
Mitigations: retries, graceful fallbacks to local logic, and smart execution (TWAP/VWAP).
Data quality: handle gaps/outliers and normalize across timeframes.
Cost: API usage grows with frequency and payload size; moderate operation is often ~US$6–20/month.
Compliance: maintain auditable logs of AI decisions, confidence scores, and inputs; disclose model limits and failure modes.
Testing & validation
Backtesting: avoid look-ahead bias and overfitting; use out-of-sample and multi-regime datasets.
Forward testing: start on demo, deploy minimal size, scale gradually on stable performance, and monitor continuously.
Engineering best practices
Resilience: robust error handling (bounded retries, timeouts, fallbacks).
Efficiency: rate-limit API calls, cache intermediate results, optimize data structures, and clean up resources.
What’s next
Tech trends: on-device/edge models (lower latency/cost), federated learning, real-time adaptation, multi-agent strategies.
Infra shifts: edge computing, 5G, and deeper cloud integration for scalable, low-latency pipelines.
Bottom line
AI can materially enhance MT5 decision quality.
Success depends on sound architecture, multi-layer risk controls, rigorous back/forward testing, active monitoring, and clear cost accounting.
Treat AI as a decision co-pilot—not an infallible oracle.
Disclaimer
Trading involves substantial risk of loss. AI systems can fail or be wrong. Past performance does not guarantee future results. Test thoroughly and never risk capital you cannot afford to lose. Educational content only; not financial advice.
Overview
Adding AI to MT5 Expert Advisors (EAs) enables more contextual, multi-signal decisions, but increases engineering complexity, cost, and governance needs.
Architecture
API integration: The EA sends market snapshots to cloud models through MT5’s WebRequest. Users must explicitly allow outbound calls and allowlist the service URL (e.g., api.openai.com).
Data model: Build a structured payload that aggregates multiple timeframes (M5/M15–M30/H1–H4/D1–W1) and key indicators (RSI, short/long EMAs, MACD, ATR, volatility, trend direction).
Multi-timeframe logic:
Short term: noise filtering and entries.
Intraday: pattern recognition.
Medium term: trend confirmation.
Long term: regime context.
This depth adds nuance but raises data and compute demands.
Regime detection & adaptation
States: trending, range-bound, high volatility, crisis.
Signals: autocorrelation and volatility stats for classification.
Position sizing: combine Kelly-style fractions (win rate/payoff) with volatility-scaled exposure to throttle risk in unstable periods.
Risk architecture
Layered controls: circuit breakers, max drawdown caps, VaR monitoring, correlation limits, daily loss limits.
Dynamic risk: adjust parameters in real time based on market state and system P&L.
Metrics: live Sharpe, Calmar, Sortino, and Expected Shortfall for risk-adjusted tracking.
Implementation challenges
Latency: API round-trips ~200–2000 ms plus model compute can cause slippage.
Mitigations: retries, graceful fallbacks to local logic, and smart execution (TWAP/VWAP).
Data quality: handle gaps/outliers and normalize across timeframes.
Cost: API usage grows with frequency and payload size; moderate operation is often ~US$6–20/month.
Compliance: maintain auditable logs of AI decisions, confidence scores, and inputs; disclose model limits and failure modes.
Testing & validation
Backtesting: avoid look-ahead bias and overfitting; use out-of-sample and multi-regime datasets.
Forward testing: start on demo, deploy minimal size, scale gradually on stable performance, and monitor continuously.
Engineering best practices
Resilience: robust error handling (bounded retries, timeouts, fallbacks).
Efficiency: rate-limit API calls, cache intermediate results, optimize data structures, and clean up resources.
What’s next
Tech trends: on-device/edge models (lower latency/cost), federated learning, real-time adaptation, multi-agent strategies.
Infra shifts: edge computing, 5G, and deeper cloud integration for scalable, low-latency pipelines.
Bottom line
AI can materially enhance MT5 decision quality.
Success depends on sound architecture, multi-layer risk controls, rigorous back/forward testing, active monitoring, and clear cost accounting.
Treat AI as a decision co-pilot—not an infallible oracle.
Disclaimer
Trading involves substantial risk of loss. AI systems can fail or be wrong. Past performance does not guarantee future results. Test thoroughly and never risk capital you cannot afford to lose. Educational content only; not financial advice.
