YouTube Ai video
Budget: ₹100 – ₹400 INR
I need a fully-automated momentum-based trading bot that works on Indian equities. The core idea is simple: scan live NSE/BSE data, detect strong upward price and volume bursts, take a position, trail a stop, and exit when the momentum fades or a profit target is hit. All logic has to be encapsulated in code so the system can run hands-free during market hours.
Key expectations
• Plug-and-play with the leading Indian broker APIs (Zerodha Kite is my first choice, but Upstox or Angel One support is a plus).
• Clean parameter file where I can tweak look-back period, entry/exit thresholds, position size, and trading window without touching the code.
• Real-time data handling, order placement, and error recovery built in.
• A concise dashboard or command-line log showing current positions, open P&L, and recent signals.
• Back-testing module that can replay at least one year of historical tick or minute data, outputting performance metrics (CAGR, max drawdown, win ratio).
• Source code, setup script, and brief usage guide delivered on completion.
Acceptance criteria
1. Strategy reproduces at least 90 % of the theoretical back-test trades when run live in paper mode for two full sessions.
2. No unhandled errors or disconnections during those sessions.
3. All parameters adjustable via the external config file and reflected instantly on restart.
Python is preferred, but I’m open to any language that meets the above targets.
Key expectations
• Plug-and-play with the leading Indian broker APIs (Zerodha Kite is my first choice, but Upstox or Angel One support is a plus).
• Clean parameter file where I can tweak look-back period, entry/exit thresholds, position size, and trading window without touching the code.
• Real-time data handling, order placement, and error recovery built in.
• A concise dashboard or command-line log showing current positions, open P&L, and recent signals.
• Back-testing module that can replay at least one year of historical tick or minute data, outputting performance metrics (CAGR, max drawdown, win ratio).
• Source code, setup script, and brief usage guide delivered on completion.
Acceptance criteria
1. Strategy reproduces at least 90 % of the theoretical back-test trades when run live in paper mode for two full sessions.
2. No unhandled errors or disconnections during those sessions.
3. All parameters adjustable via the external config file and reflected instantly on restart.
Python is preferred, but I’m open to any language that meets the above targets.