ChatGPT Trading Strategy Made 19527% Profit ( FULL TUTORIAL )

Summary

In this video, the speaker shares a refined trading strategy designed to rapidly grow a $100 investment into $10,000 using an AI-based machine learning indicator. The strategy combines the K-Nearest Neighbors algorithm with an Exponential Moving Average ribbon and a sensitive Relative Strength Index for accurate buy and sell signals. Key entry conditions for long and short trades are outlined, along with essential risk management practices, including a higher risk threshold of 5% per trade. The backtesting results demonstrate significant potential for profit, encouraging traders to test the strategy on paper accounts before committing real capital.

Takeaways

• 📈 The goal is to turn a $100 investment into $10,000 quickly using a specific trading strategy.
• 🤖 The strategy utilizes a machine learning K-N algorithm to analyze historical data and predict price movements.
• 🔍 The EMA ribbon, which consists of multiple exponential moving averages, helps identify market trends and potential entry points.
• 📊 The Relative Strength Index (RSI) is adjusted for sensitivity, aiding in confirming trade entries by indicating overbought or oversold conditions.
• 🟢 For long trades, the price must be above the 200 EMA and the EMA ribbon must be green, indicating an uptrend.
• 🔴 For short trades, the price must fall below the 200 EMA and the EMA ribbon must be red, indicating a downtrend.
• ⚖️ Risk management is emphasized, with a suggested risk of 5% per trade to maximize potential returns, despite higher drawdowns.
• 🔄 The strategy includes waiting for the candle bar to close before considering signals from the indicators as valid.
• 📈 Backtesting results showed a significant increase in the account balance to $19,527 after 100 trades, demonstrating the strategy's effectiveness.
• 📝 The creator stresses the importance of forward testing on a paper account before applying the strategy with real funds.

Q & A

What was the initial trading goal mentioned in the video?

The goal was to turn $100 into $10,000 quickly using an AI-based trading strategy.

Which indicators were used in the trading strategy?

The strategy incorporated three indicators: a machine learning K-N based strategy, an EMA ribbon, and the Relative Strength Index (RSI).

How does the machine learning K-N based strategy work?

It analyzes historical market data to predict future price movements by classifying whether a stock price is likely to rise or fall based on patterns in the data.

What is the purpose of the EMA ribbon in this strategy?

The EMA ribbon helps identify the direction and strength of market trends by plotting multiple exponential moving averages.

How is the RSI adjusted in the trading strategy?

The RSI is made more sensitive by changing the upper band to 60 and the lower band to 40 to get more valid trade entries.

What are the entry conditions for a long trade?

For a long trade, the price must close above the 200 EMA, the EMA ribbon must be green and above the 200 EMA, and the RSI must signal a buy.

What should a trader do if conditions for a short trade are met?

For a short trade, the price must fall below the 200 EMA, the EMA ribbon must be red, and the RSI must signal a sell before entering the trade.

What were the results of the backtesting for this strategy?

The strategy increased the starting balance from $100 to $19,527 after 100 trades, demonstrating its effectiveness despite higher risk.

What risk management advice is given in the video?

The user advises caution, recommending paper trading before using real money, especially due to the higher risk associated with the strategy.

What does the user imply about the risk-reward ratio of the strategy?

The user notes that while the risk per trade is higher, it can lead to higher rewards, but also mentions that it comes with the possibility of greater drawdowns.