π 2000% Profit with Bitcoin Auto-Trading! Full ChatGPT Strategy Revealed
What if I told you that itβs possible to achieve a 2000% return using an AI-powered Bitcoin auto-trading system?
Well, I tested it myself.
Starting with just 1 million KRW, I used ChatGPT to design and execute an automated trading strategy. After years of backtesting with real market data, the strategy delivered over 20 million KRW in returns. But it wasnβt a simple journey.
I didnβt have the perfect answer from the beginningβfar from it. In fact, I had to go through countless failures and mistakes to find the right approach.
In this post, Iβll reveal every detail of the auto-trading experiment.
β οΈ Read until the endβthis might change the way you trade forever.
1. The First Major Hurdle in Designing an Bitcoin Auto-Trading Strategy
The first wall I hit was the question of timeframe selection.
In a 24/7 market like crypto:
- Trade too frequently? You risk overtrading and racking up fees.
- Trade too slowly? You might miss key market moves.
So, whatβs the right tempo?
Naturally, most people assume: βFaster = better.β I was one of them.
My first idea? βLetβs try hourly trades.β
2. Preparing the Backtest: Data Collection & GPT Integration
Using APIs and Python, I collected about 10,000 BTC candlestick data points, including:
- OHLCV (Open, High, Low, Close, Volume)
- Moving Averages: 5, 20, 60, 200 days
- RSI, MACD, Bollinger Bands, MFI, Ichimoku Cloud
- Sentiment data: Fear & Greed Index, summarized news headlines
To minimize GPT API token usage, I also designed concise and precise prompts.
π§ Strategic Insights:
- Too frequent trading β excessive fees + API costs
- Too infrequent β missed opportunities
So I began testing various timeframes.
3. The Common Mistake: βIsnβt Hourly Trading Faster and Better?β
At some point, everyone thinks:
βMore data, more trades, faster response = more profit!β
So I started with 1-hour candles.
π Result: Disappointing
- Backtest from October 10, 2020
- Return: -0.54%
- Fees: ~513,000 KRW
Despite GPT making hourly decisions, the constant trading churned out fees that wiped away profits. The frequent signal noise led to false trades, reducing overall performance.
π Hourly trading = trading fees, not profits.
4. Daily Candles Next β More Stability?
Next, I tried a slower approach: 1-day candles.
π Result: Promising but Flawed
- Backtest from April 4, 2020
- Return: +1252%
- Fees: ~29,000 KRW
The strategy achieved solid growth but had its issues:
- Entered bullish trends too late
- Reacted slowly in bear markets, missing exit points
π Daily trades are safe, but you miss key moves.
5. Finally, 4-Hour Candles β The Unexpected Winner
Time to try the middle ground: 4-hour candles.
- Six decisions per day: enough to monitor trends, but not too noisy.
- Controlled fees and GPT usage.
π 4-Hour Backtest Results
- Backtest from September 17, 2020
- Return: +2000%
- Portfolio: 1M KRW β 20M+ KRW
- Fees: ~313,000 KRW
π§ Early entries during uptrends
π Quick exits in downtrends
π Re-entries during pullbacks
It traded like a seasoned proβbut it was just GPT, following a smart set of rules.
6. So What Strategy Did GPT Use?
Hereβs the logic I gave GPT:
- Buy if: 5 > 20 > 60 > 200 (Golden Cross)
- Sell if: 5 < 20 < 60 < 200 (Death Cross)
- Sell when price breaks below 20-day MA
- Re-enter on bounce near pullback zones
- Take Profit: +12%, Stop Loss: -5%
- Bollinger Band breakout β TP signal
- Consider RSI, MACD, and volume shifts
GPT made decisions every 4 hours based on this strategyβoptimized for both performance and cost-efficiency.
π Summary: Which Timeframe Delivers the Best ROI?
| Timeframe | Return | Fee Burden | Verdict |
|---|---|---|---|
| 1-hour | β Negative | High (~513K KRW) | Overtrading, poor results |
| 1-day | β Positive | Low (~29K KRW) | Stable but slow |
| 4-hour | π +2000% | Medium (~313K KRW) | Best balance of ROI & efficiency |
π§ Why Did the 4-Hour Strategy Win?
Because it:
- Captures trends without delay
- Avoids excessive trades and API costs
- Balances reaction speed and stability
While GPT has no emotions, it calmly read the market like a disciplined trader.
Thanks to this, Iβve finally seen that AI-powered technical analysis can go far beyond theoryβand generate real results.
Ready to See the Results in Action?
From now on, Iβll regularly post the AI Bitcoin auto-trading logs and strategies. Letβs monitor the real-world results together.
π Check the trade log
β οΈ This article is for informational purposes only. Investment decisions and responsibilities lie with the individual.