Generate Profitable Stock Trading Strategies with AI Chatbot in 7 Minutes

Table of Contents

• Introduction
• Coding a Trading Strategy with ChatGPT
• Backtesting and Optimizing Strategy
• Live Trading Results
• Conclusions

Introducing ChatGPT for Automated Stock Trading Strategies

In this blog post, we will explore how to leverage ChatGPT, the viral AI assistant from Anthropic, to automate profitable stock trading. Specifically, we will walk through a 7 minute process to create, backtest, and optimize a trading strategy using RSI indicators.

We will then demonstrate live trading over two days, evaluating performance and refinements to the algorithm. By the end, you will understand ChatGPT's exceptional capabilities and how to generate fully automated trades for consistent passive income.

ChatGPT's Capabilities for Quant Trading Strategies

Since its release, ChatGPT has shown abilities to pass law exams, uncover major bank vulnerabilities, and automate stock market profits. As seen in a previous video example, an RSI-moving average hybrid strategy achieved a 70% win rate. Most importantly, these algos can generate enough profits to stop working full-time jobs. Now we want to empower readers to achieve similar financial freedom.

Generating a 7 Minute Trading Strategy

In the following sections, we will provide step-by-step guidance on prompting ChatGPT to output a complete TradingView Pine Script strategy. No prior coding or trading experience is required. We simply provide the AI with a few sentences on the desired logic, such as entering long when RSI drops below 30, exiting above 70, and doing the inverse for short positions. ChatGPT handles the rest.

Coding a Scalping Strategy for S&P 500 Futures

With basics in place, we can now utilize ChatGPT to output a full Pine Script program for scalping S&P 500 futures on short timeframes. This section walks through the 7 minute process to go from prompt to backtested strategy with automated alerts.

Entering Long and Short Positions

We initialize by requesting the first line to import Pine Script, essential for TradingView. Then within the same prompt, we ask ChatGPT to enter long positions when RSI drops below 30 and close above 70. Next, we add corresponding short entries for overbought RSI readings over 70 and exits when reaching 30. At this point, all core logic is handled.

Tweaking the Strategy for Profitability

With raw inputs and exits coded, we can tweak numbers to optimize profitability and risk metrics. For example, adjusting from 30/70 to 35/65 RSI zones significantly improved the profit factor. We can also change entry durations, utilize stops, limit total concurrent trades, and more. This iterative process takes less than 30 minutes to create a backtested algorithm.

Backtesting and Optimizing Over 10+ Years

Now that we have a functioning trading program, we need to evaluate historical performance across various markets and time periods. Thankfully, TradingView enables backtesting Pine Scripts across 10+ years of intraday price data.

Checking 10-Year Profitability Across Asset Classes

By backtesting across stocks, futures, forex, and cryptocurrencies, we can measure the strategy's absolute and risk-adjusted returns. Certain assets and time periods lead to overfitting, so a deep analysis is required. We want reliable profitability metrics across decades of bull runs, recessions, flash crashes, and regular oscillations. This ensures longevity as market dynamics shift.

Automating Entry and Exit Alerts

A key benefit of TradingView is setting alerts through email, mobile notifications, webhooks, and integrations with brokerages. By activating alerts for every signal, we no longer need to actively watch charts. Positions open and close automatically based on the programmed logic.

Live Trading Results Across 2 Days of 1000+ Alerts

The true test comes from real-time performance in live markets. Over the next two sections, we will showcase the RSI scalping algorithm in action across two days and over 1000 trade alerts.

Day 1 Yields $300 on 20% Account Allocation

Remarkably, the ChatGPT program achieves $65 per $100k or 65 basis points daily despite only utilizing 20% of equity per trade. This converts to $325 at maximum account scale. The key is consistently buying on upswings and shorting overextensions around the 30 and 70 RSI levels. We achieve a 6.5% win rate across hundreds of roundtrip scalps.

Day 2 Results Plus Refinement Considerations

On the second day live trading, we generate another $50 in profits per $100k allocated. Across 253 annual trading days, that equals $12,000+ in annual returns on autopilot. The main loss comes from high volatility periods, so we consider restricting the algorithm to trade between 10:00AM to 3:00PM EST only to avoid overnight gaps and the first hour.

Concluding Thoughts on Automated Trading Bots

In closing, this real-world demonstration illuminates ChatGPT's exceptional capabilities in algorithmic trading.

Within 7 minutes, we created a profitable strategy from scratch. After tweaks and robust backtesting, the program generated $325 per day with 20% capital allocation over two live trading sessions.

Summary of ChatGPT Trading Results

The RSI scalping bot achieved consistency in entering longs near oversold levels and shorts around overbought readings. This persistence resulted in a 6.5% win rate and 65 basis points per day. Extrapolating over an entire year predicts $12,000+ profits on autopilot. More capital allocation compounds returns even further.

Potential Next Steps to Improve Performance

Future opportunities involve combining ChatGPT strategies across indicators like moving averages, Bollinger Bands, MACD histogram, etc. to boost win rates. We can also optimize entry/exit timing with machine learning and quantify how much alpha comes from the AI's code itself.

FAQ

Q: How quickly can ChatGPT generate a profitable trading strategy?
A: Incredibly, ChatGPT was able to develop a profitable trading strategy in just 7 minutes in this experiment.

Q: What was the performance of the ChatGPT trading strategy?
A: Over two days of live trading, the strategy generated $65 in profits on the first day and $100 on the second day.

Q: What timeframe was used for trading the ChatGPT strategy?
A: The strategy was traded on a short 10 second timeframe to capitalize on momentum and volatility in the markets.

Q: What trading platform was used with the ChatGPT strategy?
A: The trades were executed through TradersPost which connects to TradingView for charting and analysis.

Q: Can the ChatGPT strategy be improved further?
A: Yes, by avoiding trading during highly volatile periods at the open and close of markets, the profitability can likely be enhanced.

Q: Is coding ability required to build a ChatGPT trading strategy?
A: No coding experience is necessary. ChatGPT handles all of the programming work based simply on prompts provided to it.

Q: What was the win rate of the ChatGPT trading strategy?
A: Over the two days of testing, almost every trade was profitable, demonstrating an exceptionally high win percentage.

Q: What was the maximum drawdown observed?
A: The largest loss was $30 near the close on the second day when momentum spiked higher quickly.

Q: What types of trading strategies can ChatGPT code?
A: All types of technical analysis strategies can be generated including those based on indicators like RSI, moving averages etc.

Q: Can these AI-generated strategies outperform human traders?
A: Early results indicate significant potential for AI to develop consistently profitable strategies exceeding human performance.