AI, particularly generative AI models, has democratized the ability to create sophisticated trading strategies. Previously, developing and backtesting algorithms required extensive coding knowledge (e.g., Python, Pine Script). Now, non-coders can leverage AI tools like ChatGPT to convert their investment ideas, based on technical indicators or other logic, into executable trading strategies. This means someone without coding experience can generate code that performs as well as, or even better than, code written by a professional programmer, by simply describing their desired strategy in natural language.

TradingView is a popular platform for charting and technical analysis in the stock market. It offers a "Strategy Tester" feature that allows users to input their trading strategies and visualize their performance against historical data. This tool shows when a strategy would have bought or sold assets on a chart, and, more importantly, provides detailed analytics on profitability, maximum drawdown, winning/losing trade ratios, and overall returns. This comprehensive backtesting capability helps validate a strategy's effectiveness and build confidence before live implementation, potentially even leading to algorithmic trading.

A common simple strategy involves the 200-day Moving Average (DMA). The idea is to buy when the stock price crosses above the 200-DMA and sell when it crosses below. Before AI, testing this required downloading historical data, programming conditions, and running simulations. With AI, a non-coder can:

1. Access the source code of the 200-DMA indicator on TradingView.
2. Provide this code to an AI model (like ChatGPT) with a prompt to convert it into a TradingView strategy, explicitly stating the buy (entry) and sell (exit) conditions.
3. Paste the AI-generated Pine Script code into TradingView's Strategy Tester.

The Strategy Tester will then visually show entries and exits on the chart and provide performance metrics over a historical period, helping to determine if the strategy is profitable.

When backtesting a strategy in TradingView's Strategy Tester, several key metrics are crucial for evaluating its performance:

• Total Net Profit/Return: The overall percentage or monetary gain generated by the strategy.
• Maximum Drawdown: The largest peak-to-trough decline in the strategy's equity curve, indicating risk.
• Number of Trades: Total trades executed during the backtest period.
• Profitability Percentage: The percentage of trades that resulted in a profit.
• Average Winning Trade vs. Average Losing Trade: The average profit size of winning trades compared to the average loss size of losing trades. A significantly larger average winning trade can still lead to overall profitability even with a higher number of losing trades.
• Win-to-Loss Ratio: The ratio of winning trades to losing trades.
• Equity Performance (vs. Buy and Hold): A visual comparison of the strategy's performance (green line) against a simple buy-and-hold approach (blue line) to see if it truly outperforms the market.

While the 200-DMA strategy might seem logical, it often struggles to outperform a "buy and hold" approach over the long term, as demonstrated in the source. This is primarily due to:

• Frequent Entries/Exits: The strategy can lead to numerous small trades, especially in volatile or sideways markets.
• Transaction Costs: Each trade incurs commissions and taxes, which, when accumulated over many trades, can significantly erode profits. Even if the strategy shows a theoretical gain, these costs can make the net return similar to or even worse than simply holding the asset.
• Small Losing Trades: While winning trades might be large, a high frequency of small losing trades can negate the overall profitability.

To potentially improve such a strategy, one might introduce thresholds (e.g., requiring the price to move 3% above/below the moving average before entry/exit) to reduce false signals and minimize transaction costs.

The "Super Trend" indicator is another popular trend-following tool. It turns green in an uptrend (suggesting entry) and red in a downtrend (suggesting exit). To develop a strategy using Super Trend with AI:

1. Obtain the Super Trend indicator's Pine Script code from TradingView.
2. Feed the code to an AI model, instructing it to convert it into a TradingView strategy with specific entry (e.g., when Super Trend turns green) and exit (e.g., when Super Trend turns red) conditions.
3. Implement the AI-generated code in TradingView's Strategy Tester to backtest its performance. Initial results might require iterative refinement with the AI (e.g., by telling it the strategy isn't re-entering correctly) to optimize its logic and performance. This iterative process, even for non-coders, allows for sophisticated strategy fine-tuning.

Beyond TradingView strategies, other innovative AI-powered tools are being developed to give investors an edge:

• Shipping Data Analysis Tools: These tools analyze extensive, often paid, export-import data from shipping portals to identify companies likely to experience significant growth based on their export volumes. By processing vast amounts of real-world shipping data, these tools can provide early indicators of revenue growth for listed companies, helping investors identify potential market-beating opportunities. The tool shown, for example, considers the percentage of a company's revenue from exports to estimate its overall revenue growth from shipping data.
• Company Research Funnels: AI and automation are used to streamline the research process for companies. This involves creating multi-stage research funnels that automatically track triggers (e.g., new orders, mergers, de-listings), conduct in-depth analysis, and share comprehensive research with a community. This systematic approach ensures thorough due diligence and provides actionable insights based on real-time data and fundamental analysis.

AI's broader impact is granting "superpowers" to individual investors and non-coders, enabling them to:

• Validate Investment Ideas: Easily test their hypotheses and strategies against historical data without needing to write complex code, generating confidence in their decision-making.
• Access Sophisticated Tools: Utilize advanced analytical capabilities previously exclusive to professional traders or large institutions.
• Enhance Due Diligence: Leverage AI to process and interpret vast datasets (like shipping data) to uncover insights that would be impossible for manual analysis.
• Democratize Knowledge: Potentially convert complex investment strategies described in videos or articles into testable code, making advanced concepts accessible and verifiable. This empowers investors to make more informed, data-driven decisions, potentially beating traditional "buy and hold" approaches if strategies are well-optimized.