AI Agent for Stock Market Prediction Roadmap

Goal: Collect historical NSE/BSE stock & options data and perform Exploratory Data Analysis.

• Step 1.1: Identify Data Sources
  • Install yfinance & nsepy.
  • Read "Data Quality Challenges in Financial Time Series" (Nolte et al., 2018). [PDF/DOI]
  • Fetch 5 years of daily data for sample symbols.
• Step 1.2: Storage Setup
  • Create folders: data/raw/daily, data/raw/intraday.
  • Save CSVs and verify consistency.
• Step 1.3: Exploratory Data Analysis
  • Plot closing price series (Matplotlib).
  • Compute stats: mean return, std dev, skewness.
  • Visualize correlations with index.

Outcome: Raw dataset directory and an EDA notebook summarizing data insights.

Goal: Clean data, handle missing values, scale series, and create features.

• Step 2.1: Data Cleaning
  • Implement clean_time_series(df) to forward-fill gaps.
  • Detect outliers (Z-score) and clamp/remove.
  • Read Chapter 3 of "Practical Time Series Analysis" (Veri et al., 2020).
• Step 2.2: Scaling & Stationarity
  • Compute log-returns: np.log(df.Close).diff().
  • Fit StandardScaler and save scaler.
• Step 2.3: Feature Engineering
  • Add RSI, SMA via pandas_ta.
  • Encode cyclical time features.
  • Read "Feature Engineering for Financial Forecasting" (Zhang & Li, 2019).
• Step 2.4: Dataset Splitting
  • Split: train 70%, val 15%, test 15%.
  • Optional rolling-window CV for intraday.

Outcome: Cleaned & feature-rich datasets ready for modeling.

Goal: Build and tune forecasting models across horizons.

• Step 3.1: Baseline Models
  • Implement ARIMA with statsmodels.
  • Evaluate RMSE & directional accuracy.
• Step 3.2: Deep Learning Models
  • Build LSTM in PyTorch.
  • Read "Deep Learning for Time Series Forecasting" (Fischer & Krauss, 2018).
  • Try TCN implementation.
• Step 3.3: Hyperparameter Tuning
  • Use Optuna for tuning.
  • Log experiments in MLflow.
• Step 3.4: Multi-Horizon Strategy
  • Separate models for minute vs weekly predictions.
  • Explore N-BEATS for multi-step forecasts.
• Step 3.5: Final Model Training
  • Retrain on train+val and test hold-out.
  • Save final models and scalers.

Outcome: Trained, tuned models with documented performance.

Goal: Backtest and stress-test models for robustness.

• Step 4.1: Backtesting Loop
  • Simulate rolling predictions on test data.
  • Compute metrics (RMSE, MAPE, accuracy).
• Step 4.2: Stress Testing
  • Evaluate during volatile periods.
  • Adjust features/models if needed.
• Step 4.3: Outcome Analysis
  • Document failure modes & improvements.
  • Check accuracy targets; revisit Phase 3 if necessary.

Outcome: Validated models with actionable insights.

Goal: Deploy models locally/cloud and integrate AI agent.

• Step 5.1: Inference Script
  • Create predict.py for forecasts.
  • Test locally.
• Step 5.2: Cloud Deployment
  • Containerize & push to ECR/Registry.
  • Deploy on Lambda/Cloud Run.
• Step 5.3: Agent Integration
  • Define LangChain tools.
  • Build Streamlit/Gradio UI.

Outcome: API and interactive agent interface.

Goal: Continuously monitor, retrain, and expand.

• Step 6.1: Logging & Monitoring
  • Log predictions vs actuals daily.
  • Dashboard metrics visualization.
• Step 6.2: Automated Retraining
  • Schedule monthly retrains via scheduler.
  • Validate before deployment.
• Step 6.3: Iteration & Expansion
  • Add symbols, macro features.
  • Integrate vector DB for news.

Outcome: Self-updating, robust system.