Indian Market Stock Predictor Using AI/ML
Budget: ₹12,500 – ₹37,500 INR
Project Title
AI/ML-Based Stock Price Prediction System for Indian Stock Market (NSE/BSE)
Project Description
I am looking to hire an experienced AI/ML developer to build an end-to-end stock price prediction system specifically for the Indian stock market (NSE & BSE) using cost-effective and preferably open-source resources.
The objective is to design a robust and scalable system that predicts short-term and medium-term stock price movements by combining:
• Historical price data from NSE/BSE
• Technical indicators
• Indian financial news and sentiment analysis
• Broader Indian market and sector signals
The solution must prioritize low operational cost, while maintaining strong predictive performance. It may use models such as OpenAI (ChatGPT API), Google Gemini, or equivalent open-source ML/NLP models only where they provide clear analytical value.
⸻
Scope of Work
1. Data Collection & Pipeline (India-Focused)
The freelancer will design an automated data ingestion and update system using Indian market data sources:
Market Data:
• Historical OHLCV data (Daily & Intraday)
• From sources such as:
• NSE India APIs / website
• BSE India
• Yahoo Finance (India tickers)
• Alpha Vantage (India support)
• Quandl / RapidAPI NSE feeds
Corporate & Market Events:
• Earnings announcements
• Corporate actions (dividends, splits, bonuses)
• RBI policy announcements
• Macroeconomic indicators (CPI, GDP, IIP)
News & Sentiment:
Scrape and analyze financial news from:
• Moneycontrol
• Economic Times
• Business Standard
• Livemint
• CNBC-TV18
• Reuters India
Sentiment & event tagging must focus on India-specific market drivers.
⸻
2. Feature Engineering
Technical Indicators:
• RSI
• MACD
• Bollinger Bands
• Moving Averages (SMA/EMA)
• VWAP
• Volume & Volatility metrics
• Support/Resistance detection
Sentiment & Event Features:
• Positive/Negative sentiment score per stock
• Event-based impact tagging (earnings, RBI policy, budget, sector news)
• News frequency & sentiment intensity
Market Context:
• NIFTY 50 / SENSEX movement
• Sectoral indices (Bank Nifty, IT, Pharma, FMCG, etc.)
• FII/DII flows (if available)
⸻
3. Model Development
Models may include:
Traditional ML:
• Random Forest
• XGBoost
• LightGBM
Deep Learning:
• LSTM / GRU
• CNN-LSTM hybrid
• Transformer-based time series (if feasible within budget)
NLP Models for Indian Financial News:
• FinBERT / IndicBERT
• HuggingFace financial models
• ChatGPT / Gemini APIs for:
• News summarization
• Sentiment classification
• Event extraction
Final model selection must be justified based on:
• Predictive accuracy
• Stability
• Cost-effectiveness
• Suitability for Indian market behavior
⸻
4. Training, Validation & Testing
• Proper time-series cross-validation
• Walk-forward validation
• No data leakage
• Evaluation metrics:
• RMSE
• MAE
• Directional Accuracy
• Risk-adjusted metrics (optional)
⸻
5. Prediction Output
System should generate:
• Next-day price forecast
• 3-day and 7-day forecast
• Probability of upside/downside
• Confidence score
• Buy/Hold/Sell signal (optional)
⸻
6. Visualization & Interface
Minimum requirements:
• Interactive charts:
• Actual vs Predicted price
• Confidence bands
• News sentiment overlay
• Exportable outputs:
• CSV
• PDF
Optional:
• Simple web dashboard using Streamlit or Flask
• Mobile-friendly view
⸻
7. Cost Optimization
The solution must:
• Prefer free or low-cost APIs
• Use open-source libraries:
• Python
• Pandas
• Scikit-learn
• TensorFlow / PyTorch
• Use paid APIs (ChatGPT/Gemini) selectively only where necessary
⸻
Deliverables
1. Fully functional ML pipeline
2. Clean and documented source code
3. Trained models
4. Data ingestion scripts
5. Model evaluation report
6. Deployment-ready setup
7. User guide
⸻
Preferred Tech Stack
• Python
• Pandas / NumPy
• Scikit-learn
• TensorFlow or PyTorch
• HuggingFace Transformers
• OpenAI / Gemini API (optional)
• Streamlit or Flask
⸻
Freelancer Requirements
• Proven experience in:
• Financial ML / Quant Models
• Indian stock market data (NSE/BSE)
• Time-series forecasting
• Financial NLP
• Experience building low-cost, scalable ML pipelines
• Strong documentation practices
⸻
Budget
Open to proposals.
Preference for:
• Modular build
• Phase-wise delivery
• Cost-optimized architecture
⸻
Timeline
• Phase 1: Data Pipeline + Baseline Model – 1–2 weeks
• Phase 2: Advanced ML + NLP – 2–3 weeks
• Phase 3: Dashboard + Optimization – 1–2 weeks
⸻
Bonus (Nice to Have)
• Backtesting engine
• Auto model retraining
• Portfolio-level predictions
• Telegram alerts for Indian stocks
• FII/DII and sector rotation analysis
AI/ML-Based Stock Price Prediction System for Indian Stock Market (NSE/BSE)
Project Description
I am looking to hire an experienced AI/ML developer to build an end-to-end stock price prediction system specifically for the Indian stock market (NSE & BSE) using cost-effective and preferably open-source resources.
The objective is to design a robust and scalable system that predicts short-term and medium-term stock price movements by combining:
• Historical price data from NSE/BSE
• Technical indicators
• Indian financial news and sentiment analysis
• Broader Indian market and sector signals
The solution must prioritize low operational cost, while maintaining strong predictive performance. It may use models such as OpenAI (ChatGPT API), Google Gemini, or equivalent open-source ML/NLP models only where they provide clear analytical value.
⸻
Scope of Work
1. Data Collection & Pipeline (India-Focused)
The freelancer will design an automated data ingestion and update system using Indian market data sources:
Market Data:
• Historical OHLCV data (Daily & Intraday)
• From sources such as:
• NSE India APIs / website
• BSE India
• Yahoo Finance (India tickers)
• Alpha Vantage (India support)
• Quandl / RapidAPI NSE feeds
Corporate & Market Events:
• Earnings announcements
• Corporate actions (dividends, splits, bonuses)
• RBI policy announcements
• Macroeconomic indicators (CPI, GDP, IIP)
News & Sentiment:
Scrape and analyze financial news from:
• Moneycontrol
• Economic Times
• Business Standard
• Livemint
• CNBC-TV18
• Reuters India
Sentiment & event tagging must focus on India-specific market drivers.
⸻
2. Feature Engineering
Technical Indicators:
• RSI
• MACD
• Bollinger Bands
• Moving Averages (SMA/EMA)
• VWAP
• Volume & Volatility metrics
• Support/Resistance detection
Sentiment & Event Features:
• Positive/Negative sentiment score per stock
• Event-based impact tagging (earnings, RBI policy, budget, sector news)
• News frequency & sentiment intensity
Market Context:
• NIFTY 50 / SENSEX movement
• Sectoral indices (Bank Nifty, IT, Pharma, FMCG, etc.)
• FII/DII flows (if available)
⸻
3. Model Development
Models may include:
Traditional ML:
• Random Forest
• XGBoost
• LightGBM
Deep Learning:
• LSTM / GRU
• CNN-LSTM hybrid
• Transformer-based time series (if feasible within budget)
NLP Models for Indian Financial News:
• FinBERT / IndicBERT
• HuggingFace financial models
• ChatGPT / Gemini APIs for:
• News summarization
• Sentiment classification
• Event extraction
Final model selection must be justified based on:
• Predictive accuracy
• Stability
• Cost-effectiveness
• Suitability for Indian market behavior
⸻
4. Training, Validation & Testing
• Proper time-series cross-validation
• Walk-forward validation
• No data leakage
• Evaluation metrics:
• RMSE
• MAE
• Directional Accuracy
• Risk-adjusted metrics (optional)
⸻
5. Prediction Output
System should generate:
• Next-day price forecast
• 3-day and 7-day forecast
• Probability of upside/downside
• Confidence score
• Buy/Hold/Sell signal (optional)
⸻
6. Visualization & Interface
Minimum requirements:
• Interactive charts:
• Actual vs Predicted price
• Confidence bands
• News sentiment overlay
• Exportable outputs:
• CSV
Optional:
• Simple web dashboard using Streamlit or Flask
• Mobile-friendly view
⸻
7. Cost Optimization
The solution must:
• Prefer free or low-cost APIs
• Use open-source libraries:
• Python
• Pandas
• Scikit-learn
• TensorFlow / PyTorch
• Use paid APIs (ChatGPT/Gemini) selectively only where necessary
⸻
Deliverables
1. Fully functional ML pipeline
2. Clean and documented source code
3. Trained models
4. Data ingestion scripts
5. Model evaluation report
6. Deployment-ready setup
7. User guide
⸻
Preferred Tech Stack
• Python
• Pandas / NumPy
• Scikit-learn
• TensorFlow or PyTorch
• HuggingFace Transformers
• OpenAI / Gemini API (optional)
• Streamlit or Flask
⸻
Freelancer Requirements
• Proven experience in:
• Financial ML / Quant Models
• Indian stock market data (NSE/BSE)
• Time-series forecasting
• Financial NLP
• Experience building low-cost, scalable ML pipelines
• Strong documentation practices
⸻
Budget
Open to proposals.
Preference for:
• Modular build
• Phase-wise delivery
• Cost-optimized architecture
⸻
Timeline
• Phase 1: Data Pipeline + Baseline Model – 1–2 weeks
• Phase 2: Advanced ML + NLP – 2–3 weeks
• Phase 3: Dashboard + Optimization – 1–2 weeks
⸻
Bonus (Nice to Have)
• Backtesting engine
• Auto model retraining
• Portfolio-level predictions
• Telegram alerts for Indian stocks
• FII/DII and sector rotation analysis