Modular Stock Trading Bot
Budget: $250 – $750 USD
I need an end-to-end, desktop-based trading bot written in Python that I can grow over time without touching the core code.
The build will be fully modular:
• Scanner – pulls live data through my broker’s API, filters by ATR, pullback, RVOL, sector and overall market direction.
• Strategy Engine – applies 10-minute confirmation logic to the scanner output.
• Execution Engine – routes qualified orders to Alpaca.
• Risk Manager – hard SL, optional trailing stop, fixed $2 000 position size and a cap of two open trades at any moment.
• Alert System – pushes trade and risk notifications straight to WhatsApp.
• Trade Logger – stores every decision point so I can later train an AI model and bolt on a dashboard.
Code quality, clear separation between modules and thorough in-line documentation are essential so I can plug in new strategies, dashboards or ML tools later without rewrites.
Acceptance will be based on:
1. Running the full workflow in a paper-trading environment.
2. Verifying that limits, stops and trade caps behave exactly as described.
3. Seeing WhatsApp alerts fire in real time.
4. Confirming every trade and data point appears in the log for future AI training.
If you have experience with Python trading frameworks and Alpaca’s API, let’s get started.
The build will be fully modular:
• Scanner – pulls live data through my broker’s API, filters by ATR, pullback, RVOL, sector and overall market direction.
• Strategy Engine – applies 10-minute confirmation logic to the scanner output.
• Execution Engine – routes qualified orders to Alpaca.
• Risk Manager – hard SL, optional trailing stop, fixed $2 000 position size and a cap of two open trades at any moment.
• Alert System – pushes trade and risk notifications straight to WhatsApp.
• Trade Logger – stores every decision point so I can later train an AI model and bolt on a dashboard.
Code quality, clear separation between modules and thorough in-line documentation are essential so I can plug in new strategies, dashboards or ML tools later without rewrites.
Acceptance will be based on:
1. Running the full workflow in a paper-trading environment.
2. Verifying that limits, stops and trade caps behave exactly as described.
3. Seeing WhatsApp alerts fire in real time.
4. Confirming every trade and data point appears in the log for future AI training.
If you have experience with Python trading frameworks and Alpaca’s API, let’s get started.