TV Signal Screenerv1.1.0

Individual traders use the skill to screen stocks for technical signals like golden crosses or oversold conditions, enabling quick identification of potential entry points. They can filter by market cap and volume to focus on liquid assets, then execute trades based on real-time data without needing TradingView authentication.

Crypto investors employ the skill to screen across coins and tokens for signals such as volume breakouts or threshold crossovers, helping rebalance portfolios based on momentum. By using pre-built YAML strategies, they automate detection of trends across multiple asset classes like crypto and forex for diversified risk management.

Financial analysts at firms use the skill to generate market reports by screening bonds, futures, and stocks with custom filters and computed signals. They leverage pandas expressions and field presets to analyze large datasets, producing formatted outputs for client presentations or internal decision-making.

Quantitative developers integrate the skill into backtesting pipelines to screen assets using YAML-driven signals, testing strategies across 6 asset classes. They customize signal types like crossovers and expressions to refine models, then deploy scripts for automated scanning in live trading environments.

Finance educators and students use the skill to demonstrate market screening concepts, applying filters and technical indicators to real data without authentication hurdles. They run quick-start examples to teach asset class differences and signal detection, enhancing learning with practical, hands-on exercises.

Offer the skill as part of a cloud-based platform where users pay monthly for access to enhanced screening features, real-time alerts, and premium YAML strategies. Revenue is generated through tiered subscriptions, with higher tiers providing advanced analytics and API integrations for institutional clients.

Provide consulting services to businesses needing tailored screening solutions, such as developing custom YAML signals or integrating the skill into existing trading systems. Revenue comes from project-based fees and ongoing support contracts, leveraging the skill's flexibility for client-specific needs.

License the skill's underlying data and screening capabilities to third-party platforms, such as trading apps or financial websites, through API access. Revenue is generated via licensing fees based on usage volume or data endpoints, capitalizing on the skill's zero-auth access to TradingView data.