XAUUSD Movement Prediction with ML

@Milvetti

Hi, we can help you with your XAUUSD MA price-behavior ML model. We offer lifetime bug fix guarantee. As Milvetti, we help traders automate their strategies. Price is an estimate and may vary by scope.

@anchalsingh0005

With over 7 years of software development experience, anchelgeorge is well-versed in the intricacies of creating accurate machine learning models. Having proficiency in multiple programming languages such as R and Python, and up-to-date knowledge on technologies like Big Data Analytics and AI, I'm confident I can deliver exactly what you need for your Forex project. My experience spans across various domains including finance, where I have tackled several projects related to time series forecasting and prediction models. Specifically, in finance and trading, my proficiency extends to using only necessary features to create accurate models. This aligns perfectly with your project's requirement of relying strictly on the Moving Average and price data without any external indicators. Choosing me for this project means you are opting for clean and concise code that is well-documented every step of the way. The deliverables will not just include a well-trained machine learning model but a full-fledged explanation of the underlying methodology− essential for its real-world application. Also, with my database expertise kaoed with SQL & PHPMYADMIN, you can rest assured that I'll handle your dataset efficiently and securely.

@khaliduom103

‎Hello, ‎I am an experienced freelancer with over 1.5 years of active engagement on Freelancer.com. I hold a Ph.D. in Applied Mathematics and have strong expertise in mathematics, statistics, physics, and programming using Python and MATLAB. As a data analyst and academic expert, I can assist you with mathematical and physics-related problems, as well as provide high-quality solutions in MATLAB and Python coding. ‎ ‎

@devanshkhandekar

Hi, I am a seasoned Applied ML Engineer in FinTech(6+ yoe) & have worked extensively on high-frequency financial time-series modeling, probabilistic signal generation & leakage-safe research pipelines. In previous projects, I’ve built ML systems on market microstructure & price-action data + estimating directional probability, confidence & movement regime under strict feature constraints. That included designing pipelines on minute-level OHLCV data, creating event-based features from price behavior, handling rolling-window labeling & validating models with walk-forward / time-aware backtesting to avoid false edge from leakage. I’ve also worked on financial forecasting and anomaly/risk scoring pipelines where model outputs needed to be interpretable & deployable, with clean feature engineering, probability calibration & segment-wise evaluation. My experience largely involves converting raw price streams into structured ML inputs such as distance-to-reference, slope,velocity of approach, crossover/rejection events, reversion tendency & post-event magnitude labels. For this project, my approach would be to treat the MA as the central state variable, build features only from price–MA interaction dynamics, then benchmark models like XGBoost / LightGBM / Random Forest for continuation vs mean-reversion probability & expected move size. The final delivery would include well-documented code, a reproducible research pipeline, calibrated probability outputs & honest backtest metrics

@Algokage

Hello, I can deliver a machine learning model to analyze XAUUSD price behavior around moving averages, focusing solely on price dynamics without external indicators. I'll start with data analysis, extract features like distance and momentum relative to the MA, and develop a model using techniques such as Gradient Boosting or Random Forest. My approach ensures proper training, validation, and backtesting to avoid data leakage. With 5+ years of experience in financial market ML and time series modeling, I’ll provide clean, documented code and detailed evaluation results. Send a message to discuss further or see samples of similar work. Thanks, Adegoke. M

@prashanpatel557

Hello, I can help build a quantitative ML model to analyse XAUUSD price behaviour around a moving average, focusing strictly on the statistical relationship between price and MA without using additional indicators. Approach: 1. Data Preparation Load and clean 1-minute OHLCV data for XAUUSD. Compute the selected Moving Average (configurable period). Create event markers for approach, touch, cross, and rejection interactions. 2. Feature Engineering (MA-Only Framework) Extract features derived solely from price vs MA relationship, such as: • Distance between price and MA • Rate of approach toward MA • Angle/slope of MA and price trajectory • Duration spent above/below MA • Volatility of price around MA • Mean reversion strength after interaction events. 3. Machine Learning Model • Probability of continuation away from MA • Probability of mean reversion toward MA • Expected movement magnitude after interaction. 4. Validation Strict train/test separation to avoid leakage. Walk-forward validation to simulate real trading conditions. 5. Deliverables • Clean Python research code • Feature engineering pipeline • Trained model with probability outputs • Evaluation report and methodology documentation. The system will output directional probability and confidence scores for price behaviour relative to the moving average. Ready to discuss dataset format and MA configuration before starting.

@Shrutibimpl

Hello, I have a few queries regarding the XAUUSD price behavior model. 1) Which specific MA type and period should I use for the baseline? 2) Is the historical 1-minute data already provided in a CSV or database format? 3) What is the target timeframe for the predicted movement? I will develop a quantitative ML model using a popular gradient boosting framework to analyze the interaction between price and the MA. I will engineer features based on the distance, slope, and rate of change of price relative to the baseline. The model will be trained to recognize statistical patterns of mean reversion and continuation, providing a directional probability and confidence score for each event. This approach maintains a strict focus on price dynamics without using external indicators. Thanks, Bharat

@dohng

Hello, I’m a developer with experience in machine learning for financial time-series and quantitative trading research, and I can build a model that analyzes price behavior around a Moving Average for XAUUSD on 1-minute data. I will design a feature engineering pipeline focused strictly on price–MA interaction (distance from MA, velocity of approach, cross/touch events, deviation magnitude, and short-term momentum relative to the MA), then train models such as Gradient Boosting, Random Forest, or XGBoost to estimate probabilities of continuation vs. mean reversion and expected move size. The project will include clean data preprocessing, proper train/test separation to avoid leakage, backtesting-style validation, and probability/confidence outputs, along with well-documented Python code, model evaluation metrics, and a clear explanation of the methodology so the research can be extended later.

@rishabh6559

Hello, I am a data analyst with experience in Excel, Python, SQL, and Power BI. I specialize in data cleaning, analysis, and creating clear dashboards and reports. I can efficiently organize, analyze, and present your data to generate meaningful insights. I am committed to delivering accurate results within the deadline and maintaining clear communication throughout the project. I would be happy to work on this project and provide high-quality output.

@sanketpatil51515

Hi, I’m Sanket, a developer from Pune with 3+ years of experience working on machine learning and algorithmic trading systems. I have experience building quant models and forex Expert Advisors, so I understand price behavior and time-series modeling in trading markets. Your focus on modeling price behaviour around the Moving Average only is a strong quantitative approach, and I can build a reliable ML pipeline for this research. Approach Data Processing • Clean and structure 1-minute XAUUSD OHLCV data • Detect MA interaction events (approach, touch, cross, rejection) Feature Engineering Derived only from price–MA relationship: • Distance between price and MA • Rate of approach / divergence • Momentum relative to MA • Mean reversion patterns • Interaction event markers Model Development Models suitable for probabilistic prediction: • XGBoost / LightGBM • Random Forest • Gradient Boosting Validation • Proper train/test split • Rolling time-series validation • Backtesting to avoid data leakage Output • Probability of continuation vs mean reversion • Confidence score for movement after MA interaction • Evaluation metrics and backtesting results Deliverables • Clean Python code • Feature engineering pipeline • Trained ML model • Documentation of methodology Looking forward to collaborating on this quantitative research. — Sanket Pune | ML & Quant Developer | 3+ Years Experience

@arvindk081

Dear Client, Your project immediately caught my attention because it focuses on a pure quantitative approach to price behaviour around moving averages, which is an area I have worked extensively on in financial machine learning. I have 7+ years of experience building forecasting and predictive models using Python and machine learning, particularly for financial time-series data, algorithmic trading systems, and statistical market analysis. My work has included developing probability-based trading models, feature engineering pipelines, and backtesting frameworks for Forex, commodities, and crypto markets. Your requirement to analyze price dynamics strictly around the Moving Average without relying on additional indicators is a strong and research-driven approach. I fully understand the importance of isolating price–MA interaction mechanics to uncover statistically meaningful patterns. How I Will Approach the Project 1. Data Analysis Analyze 1-minute XAUUSD OHLCV data Study statistical behaviour of price relative to the moving average Identify patterns during: MA approach MA touch MA crossover Rejection from MA Expansion away from MA

@ranaatif1299

Hi, I have read you project description. It's look like I can help you in that. I have experience working with machine learning on time-series and financial datasets, including feature engineering and probabilistic modeling. For this project, I’d focus on extracting meaningful features strictly from the price moving average relationship (distance, slope interaction, approach velocity, cross/rejection events) and train models like XGBoost or LightGBM with proper walk-forward validation to avoid data leakage. You’ll receive a clean research pipeline, trained model, and evaluation results showing probability and confidence of price behavior around the moving average, along with clear documentation of the methodology.

@adhim41

Hello, I believe I am a strong candidate for this project because my background combines machine learning development with practical experience building data-driven systems. I have hands-on experience working with Python, Pandas, Scikit-learn, and gradient boosting models such as XGBoost and Random Forest for predictive modeling tasks. In my previous projects, I have built AI and machine learning systems involving data preprocessing, feature engineering, model training, and performance evaluation. This experience allows me to design a clean and reliable pipeline for analyzing time-series data such as financial market data. Best regards, Marpu Adhitya

@hassam945

Hi, I have strong experience in machine learning, quantitative research, and financial time-series modeling, and I can build a robust ML system to analyze price behaviour around the Moving Average for XAUUSD. I can also offer this on a done-basis approach after seeing the results, you can proceed with payment. My profile clearly shows my experience, and my portfolio includes advanced ML and AI projects involving complex data analysis and predictive modeling. This project aligns perfectly with my expertise in time-series ML and market behavior analysis. For your project, I will analyze 1-minute OHLCV data and engineer features strictly based on the relationship between price and the Moving Average, such as price-MA distance, approach velocity, interaction events (touch/cross), and mean-reversion behavior. Then I will train models like XGBoost, Random Forest, or Gradient Boosting to estimate probabilities of continuation vs mean reversion with confidence scores. You will receive clean, well-documented code, a feature engineering pipeline, trained model, and evaluation results with proper train/test separation and backtesting. I always deliver simple, structured pipelines with reliable performance. Let’s connect to discuss budget and timeline and take this project to the next level.