Machine Learning Expert in Trading
Budget: ₹1,500 – ₹12,500 INR
Project Objective:
The goal of this project is create a reusable model to evaluvate and finetune the existing stock trading strategy that leverages technical indicators by incorporating machine learning (ML) to improve the accuracy of trade entry and exit points. The ML will be used to optimize predictions for when to buy and sell, improving overall performance.
Project Scope:
1. Data Overview:
Dataset includes thousands of trades.
Each trade contains:
50 technical indicators at entry and exit (e.g., EMA, OBV, RSI).
Labels indicating if the trade was profitable.
Entry and exit points.
2. ML Model Objectives:
Classification Task: Predict whether a trade will be profitable based on the indicator values.
Optional Regression: Predict expected stock return or price movement.
Tasks to be Performed:
1. Data Preparation:
Clean and preprocess the dataset.
Normalize/scale indicators and create additional features (e.g., lag features).
2. Model Selection:
Use models like Random Forest, Gradient Boosting (XGBoost, LightGBM), Logistic Regression for classification.
Optionally, explore Neural Networks (MLP, LSTM).
Use train-test split and cross-validation for robustness.
3. Model Training & Hyperparameter Tuning:
Train models with indicators as features and trade outcomes as labels.
Tune models using GridSearchCV or RandomizedSearchCV.
4. Model Evaluation:
Evaluate models using accuracy, F1-score, precision, recall.
Provide performance reports, including feature importance analysis.
5. Backtesting:
Backtest model on historical data and report metrics like accuracy, Sharpe ratio, and maximum drawdown.
6. Documentation & Handover:
Deliver a final report with model selection rationale, hyperparameter tuning, evaluation, and backtesting results.
Provide all code and artifacts in a well-documented format.
Skills Required:
Expertise in Python and ML libraries (scikit-learn, XGBoost, LightGBM).
Experience with financial markets and technical indicators.
Knowledge of backtesting frameworks and feature engineering.
The goal of this project is create a reusable model to evaluvate and finetune the existing stock trading strategy that leverages technical indicators by incorporating machine learning (ML) to improve the accuracy of trade entry and exit points. The ML will be used to optimize predictions for when to buy and sell, improving overall performance.
Project Scope:
1. Data Overview:
Dataset includes thousands of trades.
Each trade contains:
50 technical indicators at entry and exit (e.g., EMA, OBV, RSI).
Labels indicating if the trade was profitable.
Entry and exit points.
2. ML Model Objectives:
Classification Task: Predict whether a trade will be profitable based on the indicator values.
Optional Regression: Predict expected stock return or price movement.
Tasks to be Performed:
1. Data Preparation:
Clean and preprocess the dataset.
Normalize/scale indicators and create additional features (e.g., lag features).
2. Model Selection:
Use models like Random Forest, Gradient Boosting (XGBoost, LightGBM), Logistic Regression for classification.
Optionally, explore Neural Networks (MLP, LSTM).
Use train-test split and cross-validation for robustness.
3. Model Training & Hyperparameter Tuning:
Train models with indicators as features and trade outcomes as labels.
Tune models using GridSearchCV or RandomizedSearchCV.
4. Model Evaluation:
Evaluate models using accuracy, F1-score, precision, recall.
Provide performance reports, including feature importance analysis.
5. Backtesting:
Backtest model on historical data and report metrics like accuracy, Sharpe ratio, and maximum drawdown.
6. Documentation & Handover:
Deliver a final report with model selection rationale, hyperparameter tuning, evaluation, and backtesting results.
Provide all code and artifacts in a well-documented format.
Skills Required:
Expertise in Python and ML libraries (scikit-learn, XGBoost, LightGBM).
Experience with financial markets and technical indicators.
Knowledge of backtesting frameworks and feature engineering.
Related categories:
Python
Statistics
Machine Learning (ML)
R Programming Language
Statistical Analysis