AI/ML Pipeline for Options Trading Backtest for NSE /BSE

1. Project Overview Objective: Build a robust backtesting pipeline for options trading strategies using AI/ML techniques. The pipeline will incorporate: GDFL 1-minute OHLCV data. Level 2 order book data (market depth). Computed Greeks (Delta, Gamma, Theta, Vega, etc.). AI/ML models for generating trading signals and evaluating performance. Scope: Acquire, clean, and store the relevant data. Calculate and integrate Greeks for each option contract. Engineer features from OHLCV, Level 2 order book, and Greeks. Develop AI/ML models for predictive signals or strategy optimization. Backtest and analyze strategy performance. Provide documentation, deliverable code, and a final report. 2. Key Deliverables Data Management & Integration Scripts/Processes to fetch and organize GDFL 1-min data (NSE & BSE). Integration of purchased Level 2 data into a unified format. Data cleaning, timestamp alignment, outlier detection, and basic quality checks. Greeks Computation Module A reliable script/tool to compute Greeks for each option contract (Delta, Gamma, Theta, Vega, etc.) using a standardized model (e.g., Black–Scholes). Verification checks comparing computed Greeks with reference or market data. Feature Engineering Creation of technical indicators, order book–based features (market imbalance, spread, etc.), and Greeks-based features. Documentation of all features and how they are computed. ML Model Development Selection of appropriate models (e.g., XGBoost, LSTM, Transformers, etc.) with clear rationale. Model training scripts with hyperparameter tuning and performance logging. Implementation of a rolling or walk-forward validation for time-series. Backtesting Framework A comprehensive backtesting engine that: Consumes model signals. Simulates trades (long/short option positions, hedging, etc.). Applies slippage, commissions, and partial fills based on Level 2 data. Performance metrics (PnL, Sharpe ratio, drawdown, etc.) and result visualization. Documentation & Final Report Explanation of the architecture, codebase, and user instructions. Final report summarizing: Data sources and data preparation. Model architecture and evaluation. Backtest results, performance metrics, and recommended next steps. 3. Project Phases & Timeline Below is a sample 12–16 week timeline. Adjust to match your budget, freelancer availability, and scope complexity. Phase 1: Planning & Requirements (Week 1) Goals: Finalize scope, confirm data availability, clarify technical requirements. Tasks: Kick-off meeting to discuss project goals and constraints. Align on data schemas, storage formats, and computing environment. Sign off on project plan and deliverables. Phase 2: Data Integration & Greeks Computation (Weeks 2–4) Goals: Gather all raw data (OHLCV, Level 2), integrate into a consistent format, compute and validate Greeks. Tasks: Set up data ingestion pipeline for GDFL data. Download and process Level 2 data (market depth). Create a unified database or folder structure (e.g., Parquet files). Implement Greeks calculation scripts. Perform spot checks to ensure accuracy of Greeks vs. market references. Phase 3: Feature Engineering & EDA (Weeks 5–7) Goals: Generate additional features (technical indicators, order book–derived metrics, Greek transformations) and conduct exploratory data analysis (EDA). Tasks: Define and implement feature transformations (e.g., MA, RSI, Bollinger Bands, order imbalance). Merge Greeks with other data sources to form a single dataset. Conduct EDA: visualize correlations, missing values, outliers, etc. Document feature definitions for future reference. Phase 4: Model Development & Validation (Weeks 8–10) Goals: Select ML approach (classification/regression/forecasting), train initial models, evaluate via time-series validation. Tasks: Decide on model type (XGBoost, LSTM, Transformers, etc.). Implement training scripts, including hyperparameter tuning. Set up walk-forward or rolling window validation. Evaluate model performance on training vs. validation sets (metrics: accuracy, MSE, or custom). Document findings and propose improvements. Phase 5: Backtesting & Strategy Development (Weeks 11–13) Goals: Build or refine a backtesting engine, integrate model signals, and analyze strategy performance. Tasks: Implement strategy rules based on model outputs (signals, thresholds, etc.). Incorporate transaction costs, slippage, partial fills from Level 2 data. Run end-to-end backtests on historical periods. Generate performance reports (PnL, Sharpe, drawdown, etc.). Refine strategy parameters based on test results. Phase 6: Finalization & Handover (Weeks 14–16) Goals: Finalize documentation, code repository, and present results. Tasks: Create user guides for data pipelines, model training, and backtest system. Provide final code base with version control (Git). Present final results, discuss next steps or potential live trading integration. Officially close the project; gather feedback and conduct final sign-offs. 4. Required Skill Sets for the Freelancer Data Engineering & Management Experience with time-series data ingestion, ETL pipelines, and handling large data sets. Familiarity with Parquet or similar columnar data formats, plus database technologies. Quantitative & Financial Knowledge Strong understanding of options pricing and Greeks (Black–Scholes). Experience with Level 2 order book data and market microstructure is a plus. Programming & Libraries Python (NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow, XGBoost, etc.). Familiarity with backtesting libraries or frameworks. Machine Learning & Statistics Ability to implement and tune ML models for time-series data. Knowledge of feature engineering, cross-validation, and overfitting mitigation. Project & Communication Clear communication, documentation, and version control (Git). Collaborative approach to iterating on requirements and deliverables. 5. Budget & Payment Milestones To ensure clarity and accountability, consider a milestone-based payment structure: Milestone 1 (20%) – Project Kick-off & Detailed Design Deliverables: Agreed-upon architecture, project plan, data schema mapping. Milestone 2 (20%) – Data Integration & Greeks Computation Deliverables: Working pipeline for 1-min OHLCV + Level 2 data, validated Greeks. Milestone 3 (20%) – Feature Engineering & EDA Deliverables: Feature datasets, EDA report, preliminary analysis results. Milestone 4 (20%) – Model Development & Initial Validation Deliverables: ML training scripts, validation results, performance metrics. Milestone 5 (20%) – Backtesting & Final Delivery Deliverables: Backtest engine, final performance reports, code repository, documentation.