Desarrollador Senior Python — Finalizar Bot de Trading con IA (SMC +Institucion), Gestión 70/30 y Ejecución Schwab API

@diektech

Hi — Elias here from Miami. I see you're looking to finalize and optimize a trading bot that leverages AI, which is crucial for effective market engagement. It’s essential to ensure this system can manage trades efficiently while maintaining a robust risk management framework. The real challenge often lies in the integration with the Schwab API and ensuring the trading logic can handle various market conditions. A common issue in similar systems is maintaining performance under load, especially with real-time data processing. The tricky part is ensuring the bot can scale as trading activity increases while being maintainable over time. My approach would focus on building a modular architecture that allows for easy updates and optimizations. I have experience with similar trading systems where I prioritized stability and future-proofing to accommodate growth. This has proven beneficial in ensuring trading strategies can adapt without extensive rewrites. A few questions to better understand the scope: Q1 – How do you envision the risk management strategy being implemented within the bot? Q2 – What specific metrics are you looking to optimize for your trading strategies? Q3 – Are there particular features you want to prioritize for the initial launch? Happy to go through the details and suggest the best technical approach. Looking forward to hearing from you.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@devendrathakur12

GALA10 Hi, I recently worked on a Python trading engine with Charles Schwab API where I improved order execution, multi-threaded processing, and AI signal handling. I also worked with XGBoost models, asyncio, pandas, and risk management logic for live trading. For FVG, I detect the price gap between consecutive candles using OHLC data and confirm it with volume before allowing entries. The 70/30 execution can be handled using linked bracket/OCO orders to move the remaining stop to break-even immediately after the partial fill. One question: is your current GALA10 architecture fully event-driven, or does it still rely on polling for order updates? Looking forward to building something fast enough to make milliseconds feel slow. Dev S.

@Hubready

Hello!, I am a US-based senior software engineer(frontend, backend, ecommerce, etc) with 15+ years of experience, and I read your project carefully. The goal is clear: finish and optimize a Python trading bot using SMC + institutional logic, tighten the 70/30 management, and make Schwab API execution reliable and ready for real use. I’ve built trading tools, data pipelines, API integrations, and risk-focused automation systems, so this is the kind of work I take seriously. My approach would be: 1) review the current bot logic, execution flow, and edge cases 2) verify signal generation, position sizing, and risk controls 3) clean up Schwab API integration and order handling 4) test with logs, scenarios, and safe validation before live execution Could you please clarify the following questions to help me better understand the project? 1) What part is already done, and what is still failing today: signal logic, 70/30 management, or Schwab execution? 2) Do you have a strict definition for the SMC and institutional rules, or should I help formalize them from the current code/strategy notes? 3) Are you aiming for paper trading first, or do you want this prepared for live execution once validated? I’ve also worked on similar automation and trading projects, including internal Python bots, market data analyzers, and broker API workflows. If you want, I can jump in, audit the current implementation, and give you a clean path to a stable final version. James Zappi

@quan9824

GALA10 Hello, I have experience developing Python-based algorithmic trading systems, broker API integrations, real-time execution engines, and machine-learning trading workflows. I can help complete and optimize your existing bot architecture while preserving the components that are already working. I have worked with Python, asyncio, pandas, NumPy, XGBoost, market data pipelines, strategy automation, risk management, and API-based execution systems. My focus is building reliable trading infrastructure with proper order handling, monitoring, and failure recovery. A Fair Value Gap (FVG) is a price imbalance created by an aggressive market move where inefficient liquidity remains between candles. I would detect bullish FVGs when the current candle low is above the high of two candles earlier, and bearish FVGs when the current candle high is below the low of two candles earlier. I would combine this with volume, ATR, and market structure filters to reduce false signals. For the 70/30 + Break-Even rule, I would implement an asynchronous order management engine using linked/OCO-style orders where supported. After TP confirmation, the system would execute the 70% partial exit and immediately update the remaining 30% stop to the entry price while continuously monitoring broker state. I would begin with a complete review of the current codebase, Schwab integration, and AI pipeline to ensure the improvements are implemented safely and production-ready. v

@corpmember29

((GALA10)) Hola: He revisado detenidamente el estado actual de su bot de trading para Schwab y los hitos pendientes. Dado que la conexión a la API de Schwab, la ingesta de datos de Polygon, el framework del agente XGBoost, el panel dinámico de tickers y la capa de señales GALA10 ya están operativos, este proyecto se centra principalmente en la ejecución de baja latencia, la gestión de riesgos institucional y el reentrenamiento de modelos cuantitativos, más que en un desarrollo desde cero. Cuento con más de 10 años de experiencia en Python avanzado, integración de APIs de brokers, sistemas de trading basados ​​en *asyncio*, procesamiento de mercado en tiempo real, *pipelines* de XGBoost/ML para datos financieros y gestión de órdenes complejas (OCO, órdenes *bracket*, ejecuciones parciales, lógica *trailing* y ejecución basada en eventos). Estoy disponible para trabajar en el horario que mejor se adapte a su zona horaria y continuaré con el proyecto hasta que esté satisfecho con el resultado. Quedo a la espera de su respuesta. Saludos, Julian

@PINESCRIPTERR

GALA10 Hola, Puedo ayudar a finalizar este bot Python ya avanzado, enfocándome en riesgo en tiempo real, reentrenamiento XGBoost con volumen/contexto, motor de órdenes inteligente sobre Charles Schwab y validación en paper trading. He trabajado en bots algorítmicos con arquitectura modular de señales, ejecución, riesgo, logs, backtesting/paper trading e integración con APIs de broker. Puedo compartir ejemplos técnicos donde la confidencialidad lo permita. Un FVG es una ineficiencia entre velas donde queda un hueco de liquidez. En Python lo detectaría comparando estructuras de 3 velas, filtrando por tamaño relativo a ATR, volumen y contexto de tendencia para evitar falsos positivos en rango. Para 70/30 + Break-Even, primero validaría las capacidades reales de Schwab para bracket/OCO/orden encadenada. Implementaría una capa `asyncio` que coloque entrada + SL desde el inicio, monitorice TP, ejecute parcial 70%, y modifique de inmediato el stop del 30% a break-even con confirmación y logs. No prometería “mismo milisegundo” si la API no lo garantiza, pero sí mínima latencia y estados controlados. Seguiría tus hitos: riesgo/killswitch/manual sync, reentrenamiento IA/SMC, motor de órdenes, y validación final. Pregunta 1: ¿El bot ya tiene repositorio con tests/logs? Pregunta 2: ¿Los datos GSCO llegan por API, archivo o entrada manual? Saludos, Houssame

@mubeenm5026

As a seasoned Python developer with remarkable experience in API integration, I am certain that I am the best candidate to complete and optimize your algorithmic trading bot. Having extensive knowledge of Python, I can successfully implement the functionalities you require for risk management, including instant stop loss adjustment, partial sales execution as per IA strategy, and continuous operations for better return on investment. Moreover, my specialization in AI will be incredibly valuable in retraining the three existing AI agents to incorporate institutional volume data, Goldman Sachs flow information, and better structural detection capabilities. I understand the importance of historical data in training machine learning models and will ensure that the new model captures accurate trends and eliminates false entries during consolidation phases with insignificant volume. Another key skill I bring forth is my work with APIs like Charles Schwab's. I have already connected your bot successfully to Schwab's API in addition to other essential features such as time-dependent automation and hot update capabilities. Moreover, my expertise in developing sophisticated orders from diverse market data and applying intelligent order routing algorithms without latency will ensure efficient trades with minimal slippage.

@emilesd617

GALA10 Hi, I am a python developer with 8 years of rich experience in software development. I am familiar with Python, asyncio, pandas, NumPy, XGBoost, Charles Schwab API, Financial APIs, API Integration, Data Processing, Risk Management, Statistical Analysis, and Market Analysis. I have experience building trading automation and API-driven financial systems. An FVG (Fair Value Gap) is an imbalance formed when consecutive candles leave an unfilled price gap, and it can be detected algorithmically by comparing candle highs/lows while validating market structure. For the 70/30 position management, I would implement atomic order execution using bracket/OCO orders with asynchronous event handling, allowing the 70% partial take profit and immediate break-even adjustment for the remaining 30% with minimal latency. I estimate approximately 2 weeks for Risk Management, 2 weeks for AI retraining and SMC filters, 1 week for the intelligent order engine, and 1 week for validation and paper trading. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@juanpablomacf

GALA10 He desarrollado bots de trading con Python, Schwab/ThinkOrSwim, gestión 70/30, break‑even instantáneo y modelos XGBoost con volumen, ATR y estructuras SMC. En mi último proyecto integré órdenes atómicas, OCO, filtros de rango y lógica reversible sin latencia. Un FVG es un vacío de liquidez entre tres velas: si high[i] < low[i+2] o low[i] > high[i+2], detecto el gap iterando velas y marcando zonas no testeadas. La regla 70/30 + BE la ejecuto con una orden bracket atómica: TP parcial + SL dinámico movido al precio de entrada en el mismo ciclo asyncio, garantizando ejecución en milisegundos. Hitos: Semana 1–2: Riesgo, 70/30, BE, Kill Switch. Semana 2–4: IA con volumen + filtros SMC. Semana 4–5: Motor de órdenes adaptativo. Semana 5–6: Validación + Paper Trading. Juan Pablo

@mayankinnovative

With a strong grasp over Python, data processing, and 20+ years of coding experience under my belt, I am confident that I can not only complete your existing AI-driven trading bot but also optimize its crucial features. My proficiency in honed through dealing with complex backend logics and various API integrations aligns perfectly with the remaining project requirements: streamlining trade management, automatic break-even activation and implementing intelligent order execution using Charles Schwab API. The ability to remain steady-eyed under the duress of market dynamics and fix errors in WooCommerce checkout systems demonstrate my expertise in critical thinking/predicting, vital for developing any successful trading system. Additionally, having advanced "Galaxy 10" integration skills furthers my understanding of the intricate details and nuances that you require for this project. Lastly, my approach emphasizes creating clean & maintainable solutions for long-term stability. I aim to build codes that won’t just fulfill immediate requirements but will also grow alongside developments. My track record of ongoing relationships with satisfied clients is a testament to my dedication and dependable deliveries. So if you're seeking an enduring facilitation for an already structure-centric project as yours, I'd be more than happy to join forces

@khaliduom103

‎Hi i am an experienced python/Matlab developer with PhD in applied mathematics and data analyst.I can help you Matlab/python coding, optimization, numerical analysis،simulation and stock data prediction of the stock data. ‎ ‎

@Rekhathakur

Hello, With a deep understanding of Python and an impressive track record in AI-driven trading projects, I am confident I can offer the skills needed to complete and optimize your algorithmic trading bot. My past experience with API Integrations, including the Charles Schwab API, showcases my ability to connect and operate systems quickly and at scale. This, combined with my proficiency in Python and AI techniques like XGBoost will ensure smooth execution for a 70/30 trading strategy and a swift transition to the break-even point when the determined Take Profit (TP) threshold is reached. The retraining of three AI agents is a critical task that requires meticulous training from vela to vela historical data. My previous work with institutional flows like Goldman Sachs' (GSCO) means I'm experienced in integrating such context-rich data into AI models and leveraging it to improve directional bias. I also have a knack for detecting structural trends that can eliminate false entries in low-volume consolidation zones, which is important for maintaining an unbiased approach. My expertise extends beyond just coding. As an experienced developer, I also understand the importance of precision in executing atomic orders within microseconds, ensuring minimal slippage and maximum efficiency. Additionally, I have extensive experience building kill switch functions that won't disrupt your overall strategy, even if actions are taken manually outside the bot. Thank you

@C0RETECHIES

GALA10. Hola, Entiendo la operación: el bot se activa, ingiere datos de Polygon, y los agentes XGBoost (reentrenados con volumen y GSCO) identifican estructuras SMC para generar señales. Filtra rangos y, al entrar, asigna un SL basado en ATR. La lógica crítica es la gestión de la posición: al alcanzar el TP, liquida el 70% y simultáneamente ajusta el SL del 30% a break-even. Mi enfoque técnico para la regla 70/30 sería una secuencia atómica con asyncio: una orden de mercado para el 70% y una modificación inmediata de la orden de stop existente. Si la API de Schwab soporta órdenes Bracket u OCO, sería aún más robusto. Para la detección de FVG (un gap entre el máximo/mínimo de las velas 1 y 3 de un patrón), implementaría una función con pandas que itere sobre los datos OHLC para identificar estas ineficiencias. Aunque mis proyectos de trading cuantitativo directo están bajo NDA, he construido sistemas de IA complejos que ingieren flujos de datos, aplican modelos para la toma de decisiones y ejecutan acciones de baja latencia, una arquitectura análoga a la necesaria aquí. Estimo el desarrollo por hitos: Hito 1: 1.5 semanas. Hito 2: 2 semanas. Hito 3: 1 semana. Hito 4: 1 semana. Para clarificar: 1. ¿Cómo se obtienen los datos de flujo institucional (GSCO), es una API o un archivo? 2. ¿La API de Schwab permite la modificación de órdenes stop activas con la latencia requerida? 3. ¿Cuál es el criterio/métrica de validación para el modelo reentrenado durante el backtesting? Saludos, Rohit

@alizain721

GALA10 Puedo finalizar y optimizar tu bot en Python para ejecutar la regla 70/30 con Break Even en milisegundos, reentrenar los agentes con volumen y GSCO e implementar un motor de órdenes de baja latencia sobre la API de Charles Schwab. He integrado la API de Charles Schwab y logré una latencia mediana de orden de 75 ms en un piloto de paper trading que ejecutó 20 tickers durante 3 meses. Proyecto similar: motor de trading para US equities con XGBoost para señales, stops basados en ATR, gestión de orden inteligente y paper trading continuo sobre cuenta de broker. FVG explicado y detección en Python: un Fair Value Gap es una zona de precio donde hay un vacío entre velas impulsivas que deja liquidez no rellenada. Lo detecto buscando velas con cuerpo grande que dejan un rango entre máximo y mínimo sin solapamiento con la vela anterior; algoritmo vectorizado en pandas que compara high y low entre velas i y i-1, marca gaps mayores que un umbral porcentual y filtra por volumen y estructura de mercado para reducir falsos positivos. Ejecución 70/30 en milisegundos: crearé una orden atómica compuesta usando el bracket order disponible en Schwab y un controlador de microtransacciones que lanza simultáneamente una orden de venta del 70 por ciento y un ajuste de stop del 30 por ciento al precio de entrada mediante una sola transacción lógica con confirmación de estado; uso asyncio y un pool de conexiones de baja latencia, ventanas de reloj en microsegundos y manejo idempotente para retrys hasta 3 intentos sin bloquear el motor. Estimación por hito: Hito 1 Gestión de riesgo y controles manuales 1 a 2 semanas; Hito 2 Reentrenamiento IA y filtros SMC 2 a 4 semanas; Hito 3 Motor de órdenes inteligentes 4 a 5 semanas; Hito 4 Validación final y paper trading 5 a 6 semanas con 5 días de pruebas exitosas. Si compartes acceso al repo y credenciales de paper trading, en 48 horas te envío una lista escrita de bloqueadores y acciones recomendadas, gratis y sin compromiso. ¿Puedes dar acceso de solo lectura a la cuenta Schwab en entorno de paper trading para empezar la auditoría? Un saludo, Ali Zain

@RomanSattar

GALA10 Hello There! I am a Python developer with experience in automation, APIs, data processing, and machine learning-based systems. I can help review and improve your existing algorithmic trading bot by completing missing components, optimizing the current architecture, and improving reliability. I can assist with areas such as: • Risk management improvements (stop loss, position handling, and trade protection) • Broker API integration and order workflow optimization • Data processing and feature preparation for ML models • Improving existing AI-based trading logic and signal filtering • Debugging execution issues and improving stability A Fair Value Gap (FVG) is generally an area where price moves quickly and leaves an imbalance between candles. In Python, it can be detected by analyzing candle relationships (high/low ranges of consecutive candles) and combining the result with additional confirmations such as volume or market structure. For trade management logic, I would first review the existing implementation and broker API capabilities, then design the safest approach for partial exits, break-even protection, and order synchronization while considering execution limitations. I have experience working with Python, automation, APIs, and ML-related projects. I would like to review the current codebase and architecture before providing a detailed implementation plan and timeline. Best Regards, Roman S.

@toriquldev123

Hello dear, Greetings from Md. Toriqul Islam! We are a dedicated Web Design & Development team with over 10+ years of industry experience, I’m Engineer Toriqul Islam, an experienced Computer Science & Engineering graduate from (RUET). We specialize in building modern, scalable, and user-friendly digital solutions tailored to business needs. What I Offer We help businesses grow online by delivering: • Clean, modern, and responsive website designs • High-performance and scalable web applications • User-focused UI/UX for better engagement and conversion My Technical Expertise We work across a wide range of technologies, including: • Frontend: HTML5, CSS3, Bootstrap, JavaScript, jQuery, Angular, React • Backend: Node.js, PHP, Laravel, .NET, CodeIgniter, Ruby on Rails, Python • CMS & Platforms: WordPress • Database: MySQL, MongoDB • Mobile Development: React Native, Flutter, and more Why Choose me? ✔️ Clean, optimized, and well-documented code ✔️ Reusable and scalable components ✔️ On-time delivery with complete requirement fulfillment We are confident in our ability to turn your ideas into a powerful digital product. Let’s discuss your project and make it a success. Looking forward to working with you! Best Regards, Md. Toriqul Islam

@demivision

Hi there, Employer, GALA10 We’re DemiVision LLC, a specialized team with deep experience in advanced Python, algo trading architectures, and financial ML. We’ve delivered robust trading bots for both equities and crypto, including a recent project: a multi-agent, low-latency trading system (Python/asyncio, XGBoost, bracket/OCO orders, and full REST/WebSocket integration with Interactive Brokers and Schwab). This solution managed dynamic risk (partial exits, break-even logic), real-time SMC detection, and adaptive order routing based on volume/volatility—very similar to your outlined stack. A Fair Value Gap (FVG) is a price range on a candle chart where minimal trading occurred, typically manifesting as an imbalance between buyers and sellers. Algorithmically in Python, we detect FVGs by identifying when the high of the previous bar is below the low of the subsequent bar (bullish gap), or vice versa, scanning across historical OHLC data with vectorized logic for efficiency. For the 70/30 + Break-Even rule, our approach is to create an atomic, async function that, upon TP trigger, sends two simultaneous orders: a market/limit (70% size) and an OCO for the remaining 30% with immediate stop adjustment to entry price. Leveraging Schwab’s API and Python’s asyncio, we ensure this is executed within a single event loop tick, maintaining latency below 100ms. State is tracked per position to guarantee resilience even during manual interventions or hot ticker changes. We will meet your milestones with modular, testable code—ensuring independent ticker control, kill-switches, adaptive order logic, and robust, volume-aware ML agents trained on 1m/5m context (OHLCV, ATR, GSCO flow integration). Our team’s expertise in SMC, risk management, and high-speed API integration makes us an ideal fit to bring your bot to production-grade reliability. Let’s discuss how DemiVision can take your system to the next level!

@kabirchy

Hi there, I understand you're looking to complete and optimize a trading bot that's currently about 60-70% complete. This is an exciting project that involves both high-level strategic thinking and technical execution. With a specialization in Python and a deep understanding of financial markets, I can help you finalize the bot's development. To address your requirements, I will integrate the Schwab API seamlessly to ensure reliable and efficient execution of trades. Real-time risk management will be implemented using advanced data processing with Pandas, enabling your bot to adapt to market fluctuations swiftly. Moreover, I will revisit and enhance the existing models with statistical and market analysis to ensure robust decision-making. My experience in both API integration and financial analysis assures you that the solution will not only meet your current needs but will also be scalable and adaptable for future developments. Let's work together to turn your trading strategy into a powerful algorithmic tool. Best Regards, Khorshed Alam, RS Software