Algorithmic Stock Signal & Risk Tool
Budget: $10 – $30 USD
I want to build a desktop or web-based tool that scans the stock market in real time, identifies fresh buy opportunities, and immediately shows three pieces of information for each trade:
• the precise entry signal,
• a clearly marked take-profit zone, and
• a suggested stop-loss level calculated from recent volatility.
All decisions must be generated by an algorithmic trading model—no discretionary or news-based filters—so the entire workflow remains fully systematic and repeatable. I am open to your preferred stack (Python with pandas/TALib, C# with .NET, or another proven environment) as long as the finished product reliably connects to live or end-of-day equity data and runs on Windows.
Because I trade only stocks, you can tune the logic specifically for equity market behaviour: session gaps, daily volume profiles, and tick size quirks matter more to me than multi-asset generality. Trend-following, mean-reversion, or hybrid logic is fine; what counts is transparent code, back-test reports, and the ability to tweak parameters without rewriting core functions.
There is no hard deadline, so we can agree on sensible milestones—research & model design, prototype with back-test, live data hookup, and final UI polish—letting you focus on robustness rather than speed.
Deliverables
1. Well-commented source code and a runnable application (installer or Docker image).
2. Documentation covering setup, parameter explanations, and a sample watch-list configuration.
3. Back-test results (CSV or PDF) that match the default settings shipped with the software.
I will test the tool on my own brokerage feed; payment releases once it reproduces the entry/exit levels shown in your back-tests within an acceptable slippage band. If you’re confident with algorithmic equity strategies and enjoy building clean, reusable code, let’s discuss your plan of attack.
• the precise entry signal,
• a clearly marked take-profit zone, and
• a suggested stop-loss level calculated from recent volatility.
All decisions must be generated by an algorithmic trading model—no discretionary or news-based filters—so the entire workflow remains fully systematic and repeatable. I am open to your preferred stack (Python with pandas/TALib, C# with .NET, or another proven environment) as long as the finished product reliably connects to live or end-of-day equity data and runs on Windows.
Because I trade only stocks, you can tune the logic specifically for equity market behaviour: session gaps, daily volume profiles, and tick size quirks matter more to me than multi-asset generality. Trend-following, mean-reversion, or hybrid logic is fine; what counts is transparent code, back-test reports, and the ability to tweak parameters without rewriting core functions.
There is no hard deadline, so we can agree on sensible milestones—research & model design, prototype with back-test, live data hookup, and final UI polish—letting you focus on robustness rather than speed.
Deliverables
1. Well-commented source code and a runnable application (installer or Docker image).
2. Documentation covering setup, parameter explanations, and a sample watch-list configuration.
3. Back-test results (CSV or PDF) that match the default settings shipped with the software.
I will test the tool on my own brokerage feed; payment releases once it reproduces the entry/exit levels shown in your back-tests within an acceptable slippage band. If you’re confident with algorithmic equity strategies and enjoy building clean, reusable code, let’s discuss your plan of attack.