Trend-Following Stock Auto Trader
Budget: $250 – $750 USD
I need a complete, hands-free trading algorithm that follows a trend-following logic and executes live orders automatically on both the NYSE and the South African equities market. The core of the job is to translate a rules-based trend detection approach into production-ready code, connect it to a broker or exchange API, and keep it running without manual intervention once deployed.
The system must:
• Detect qualified up-trends in real time, trigger entries, manage positions, and close them automatically in line with the strategy’s rules.
• Work seamlessly on NYSE-listed stocks as well as South African names; please factor any symbol-mapping or timezone differences into your architecture.
• Include straightforward parameters for position sizing, maximum open risk, and daily stop so I can fine-tune exposure without touching the codebase.
• Log every trade with timestamp, price, size, and P&L to a CSV or lightweight database so I can audit performance.
Deliverables are the fully documented source code, a short setup guide so I can install the environment on my own machine or server, and at least one month of historical back-test metrics that demonstrate the strategy’s behavior.
Language, framework, and data-feed choices are up to you—Python with libraries like pandas, TA-Lib, and an API wrapper such as Alpaca or Interactive Brokers is perfectly fine, but I’m open to alternatives if they simplify cross-exchange execution.
Once the algorithm meets its acceptance criteria—accurate order routing on both markets, correct risk limits, and reproducible back-test results—I’ll sign off and move to live trading.
The system must:
• Detect qualified up-trends in real time, trigger entries, manage positions, and close them automatically in line with the strategy’s rules.
• Work seamlessly on NYSE-listed stocks as well as South African names; please factor any symbol-mapping or timezone differences into your architecture.
• Include straightforward parameters for position sizing, maximum open risk, and daily stop so I can fine-tune exposure without touching the codebase.
• Log every trade with timestamp, price, size, and P&L to a CSV or lightweight database so I can audit performance.
Deliverables are the fully documented source code, a short setup guide so I can install the environment on my own machine or server, and at least one month of historical back-test metrics that demonstrate the strategy’s behavior.
Language, framework, and data-feed choices are up to you—Python with libraries like pandas, TA-Lib, and an API wrapper such as Alpaca or Interactive Brokers is perfectly fine, but I’m open to alternatives if they simplify cross-exchange execution.
Once the algorithm meets its acceptance criteria—accurate order routing on both markets, correct risk limits, and reproducible back-test results—I’ll sign off and move to live trading.
Related categories:
C Programming
Python
Algorithm
Metatrader
C++ Programming
Financial Analysis
Data Analysis
API Integration