Automated NIFTY Options Trading Python Developer

Development of an Automated NIFTY Options Trading System (Python)

Project Overview

I am looking for an experienced Python algorithmic trading developer to build a fully automated trading system for NIFTY options.

The trading strategy has already been designed. I need someone who can convert the trading logic into clean, modular, scalable, and well-documented code.


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Strategy Overview

The strategy is primarily based on Price Action and Support/Resistance, not conventional indicators.

Market Context

- NIFTY Index is used to determine the overall market direction and context.
- Option charts (CE/PE) are used for actual trade execution.
- A trade is taken only when the NIFTY and the selected option are aligned.

Core Concepts

The strategy includes:

- Support and Resistance zones
- Role reversal (Support becomes Resistance and vice versa)
- Swing highs and swing lows
- Price Action
- Rejection candles
- Confirmation candles
- Liquidity sweeps / breakout failures
- Volume confirmation
- Risk-Reward filtering
- Higher timeframe bias
- Multi-timeframe confirmation

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Trading Logic

The exact rules will be shared after selection.

The system should be capable of:

- Identifying valid support and resistance zones
- Classifying zones as support or resistance
- Detecting price interaction with zones
- Recognizing predefined candle patterns
- Validating trades using market context
- Calculating Entry
- Calculating Stop Loss
- Calculating Target
- Calculating Risk-Reward ratio
- Filtering low-quality setups

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Fallback Logic

If no valid option support/resistance levels are available, the strategy should automatically switch to an alternate execution model based on:

- EMA crossover
- NIFTY directional confirmation
- Price action confirmation

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Features Required

Market Data

- Live market data
- Historical data
- Expired options data (for backtesting)
- Multiple timeframes

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Scanner

The system should continuously scan:

- NIFTY
- CE options
- PE options

and identify valid trading opportunities.

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Trade Management

Automatic calculation of:

- Entry Price
- Stop Loss
- Target
- Position Size
- Maximum Risk
- Risk-Reward Ratio

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Order Management

- Paper trading mode
- Live trading mode
- Automatic order placement
- Stop Loss order
- Target order
- Trailing Stop Loss
- Exit conditions
- Manual override

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Risk Management

Configurable:

- Maximum daily loss
- Maximum trades per day
- Maximum risk per trade
- Consecutive loss limits
- Trading time window
- News/event filters (future enhancement)

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Dashboard

A clean dashboard displaying:

- Current signals
- Active trades
- P&L
- Win rate
- Risk-Reward
- Trade history
- Account statistics
- System status

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Alerts

Notifications through:

- Telegram
- Desktop notifications
- Email (optional)

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Backtesting

Ability to backtest using historical NIFTY and options data with reports including:

- Win rate
- Profit factor
- Drawdown
- Expectancy
- Monthly returns
- Equity curve
- Trade log

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Reporting

Generate detailed reports showing:

- Daily performance
- Weekly performance
- Monthly performance
- Trade screenshots (optional)
- Analytics

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Technical Requirements

Preferred Technology Stack:

- Python
- Broker API integration (e.g., SmartAPI, Zerodha Kite, or similar)
- Pandas
- NumPy
- TA libraries (only where required)
- SQLite/PostgreSQL
- Git version control

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Code Requirements

The project should be:

- Modular
- Object-oriented where appropriate
- Well documented
- Easy to maintain
- Easily extendable
- Efficient and optimized
- Properly logged
- Exception handled
- Configurable through external configuration files

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Deliverables

The selected developer should provide:

- Complete source code
- Installation guide
- Documentation
- Configuration guide
- VPS deployment guide
- Backtesting module
- Live trading module
- Paper trading module
- User manual

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Ideal Candidate

Looking for someone with experience in:

- Python development
- Algorithmic trading
- NIFTY/F&O trading
- Broker API integration
- Backtesting frameworks
- Automated trading systems
- VPS deployment
- Clean software architecture

Please include examples or GitHub repositories of similar trading or algorithmic projects you have built.

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Project Scope

This project is expected to evolve over time. The initial goal is to build a stable Version 1.0, followed by enhancements such as advanced market structure analysis, AI-assisted trade filtering, additional strategy modules, improved analytics, and portfolio-level risk management.

I am looking for a reliable long-term collaborator rather than someone interested only in a one-time project.