In the fast-evolving world of cryptocurrency trading, artificial intelligence (AI) is no longer a futuristic concept—it's a practical tool reshaping how investors approach the markets. One standout example is Rekt Fencer, a pseudonymous trader on X (formerly Twitter), who claims to have generated over $71,500 in profits using a custom-built AI-assisted trading bot. This story isn’t just about big gains—it highlights how accessible tools like ChatGPT, combined with technical analysis indicators, can empower individual traders to automate sophisticated strategies.
The Rise of AI in Crypto Trading
AI is increasingly being leveraged to analyze vast datasets, detect market patterns, and execute trades at speeds and precision beyond human capability. In crypto, where volatility is both a risk and an opportunity, AI-driven systems offer a competitive edge. Rekt Fencer’s success demonstrates how retail investors can harness these technologies without needing a background in quantitative finance or machine learning.
His strategy centers around algorithmic trading, where predefined rules trigger buy and sell decisions. By integrating ChatGPT into the development process, he streamlined coding tasks, debugged errors efficiently, and optimized his system for real-world performance.
Building the Trading Bot: A Step-by-Step Breakdown
Rekt Fencer’s bot relies on one of the most widely used technical indicators in trading: the Bollinger Bands. This tool measures price volatility by plotting two standard deviations above and below a moving average. When prices breach the upper or lower bands, it often signals potential reversals or continuation patterns—insights traders can exploit.
1. Strategy Design Using Bollinger Bands
The core logic of the bot is simple yet effective:
- Go long (buy) when the price closes above the upper Bollinger Band.
- Go short (sell) when the price closes below the lower Bollinger Band.
- Close existing positions when the opposite condition is met.
This approach capitalizes on market volatility, aiming to ride momentum while minimizing exposure during consolidation phases.
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2. Coding the Bot with Pine Script v5
Rekt Fencer used TradingView, a popular charting platform, to implement his strategy using Pine Script v5—the platform’s proprietary scripting language. Instead of writing complex code from scratch, he turned to ChatGPT for assistance.
He provided a clear prompt:
“Code strategy entries for this in pinescript v5. Enter a long when the price closes above the bollinger band, and short when price closes below the lower bollinger band. Close the long when the short condition is met, and close the short when the long condition is met.”
Then, he pasted the existing indicator’s source code beneath the prompt, allowing ChatGPT to generate compatible logic that automates trade entries and exits.
3. Debugging Errors with AI Assistance
One of the biggest hurdles in coding—even for experienced developers—is troubleshooting errors. Red error messages in TradingView can be frustrating, especially for beginners.
Fencer shared a practical workflow:
- Copy the error message.
- Paste it into ChatGPT.
- Use the corrected code suggestion and replace it in TradingView.
- Repeat until all issues are resolved.
This method significantly reduces development time and lowers the barrier to entry for non-programmers.
“Don’t panic when you see red errors… Trust me, you will finally resolve all errors,” he tweeted, emphasizing persistence and the power of AI as a coding assistant.
Connecting to a Crypto Exchange for Automation
To move from backtesting to live trading, users must link their crypto exchange accounts to TradingView via webhooks or third-party automation tools. Once connected, the bot can send real-time trade signals directly to the exchange, enabling fully automated execution based on market conditions.
However, this integration requires careful setup and security considerations—especially since it involves live funds.
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Risk Management: The Key to Sustainable Success
While Rekt Fencer’s results are impressive, he stresses the importance of risk management. He advises new traders to:
- Start with small position sizes.
- Test strategies thoroughly in demo mode.
- Monitor performance before scaling up.
Automated systems can amplify both gains and losses. Without proper controls—like stop-losses, position sizing rules, and regular reviews—bots can lead to significant drawdowns during unexpected market moves.
Core Keywords Driving This Strategy
Understanding key concepts is essential for replicating success. The core keywords tied to this approach include:
- AI trading bot
- ChatGPT crypto trading
- Bollinger Bands strategy
- Pine Script automation
- automated crypto trading
- market volatility exploitation
- algorithmic trading
- TradingView integration
These terms reflect both the technical components and search intent behind users exploring AI-powered trading solutions.
Frequently Asked Questions (FAQ)
Can I really make money with an AI-powered crypto trading bot?
Yes, it’s possible—but not guaranteed. While Rekt Fencer made over $71,500, results vary based on market conditions, strategy quality, risk management, and execution. Many bots lose money if poorly designed or improperly monitored.
Do I need programming experience to build a trading bot?
Not necessarily. Tools like ChatGPT and TradingView’s visual editor allow beginners to generate and test code without deep coding knowledge. However, understanding basic logic and financial concepts is crucial.
Is using ChatGPT for coding trading strategies safe?
ChatGPT helps write and debug code quickly, but always verify its output. Never run untested scripts with real funds. Use sandbox environments first and double-check logic for unintended behaviors.
How do Bollinger Bands work in volatile markets?
Bollinger Bands expand and contract with volatility. In high-volatility periods, breakouts beyond the bands may indicate strong momentum. Traders use these signals to enter or exit positions based on trend strength.
Can I automate trades directly from TradingView?
Yes, through alerts and webhook integrations with exchanges like OKX or Binance. You’ll need to configure API keys securely and ensure your strategy is tested before going live.
What are the risks of automated crypto trading?
Risks include technical failures, market slippage, overfitting backtests, and flash crashes. Always use risk controls like maximum position size, daily loss limits, and emergency shutdown protocols.
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Final Thoughts: AI as a Trader’s Ally
Rekt Fencer’s journey shows that AI isn’t replacing traders—it’s empowering them. With platforms like ChatGPT handling repetitive coding tasks and TradingView offering robust analytics, individuals can focus on strategy refinement rather than syntax debugging.
However, technology alone isn’t enough. Long-term success depends on disciplined risk management, continuous learning, and adapting to changing market dynamics.
As AI continues to evolve, its role in automated crypto trading will only grow—offering more accessible, intelligent tools for traders at every level. Whether you're building your first bot or refining an advanced system, combining human insight with machine efficiency could be the key to unlocking new opportunities in 2025 and beyond.