ChatGPT Trading Strategy Made 19527% Profit ( FULL TUTORIAL )

Introduction

Turning $ 100 into $ 10,000 rapidly has long been a dream for many traders, and with a solid trading strategy combined with advanced technology, it became possible. In this article, we'll walk you through a detailed trading strategy that leverages machine learning indicators in TradingView, specifically focusing on Ethereum on a three-minute timeframe. This strategy, tested over 100 trades, yielded staggering results, demonstrating a 19,527% profit. Below, we’ll break down the entire setup, including the indicators involved, entry conditions, backtesting results, and necessary precautions.

Step 1: Setting Up Trading Indicators

To start, we will be using three free TradingView indicators. These tools work synergistically to create a robust trading strategy:

1. Machine Learning K-N Based Strategy

   • Created by Capissimo, this indicator analyzes historical market data to predict the future direction of price movements. Utilizing the K-N classification algorithm, it categorizes stock prices based on patterns found in historical data. The machine learning aspect ensures that the indicator does not repaint, but one must wait for the candle bar to close for a valid signal. Buy signals are indicated by blue labels, while sell signals use pink labels.

2. EMA Ribbon by Dominic Osceleti

   • This indicator plots several exponential moving averages (EMAs) to illustrate market trends. The ribbon-like appearance helps traders identify the strength and direction of trends. When the ribbon slopes upward, it indicates an uptrend and vice versa. To reduce noise in our signals, we will disable its native buy/sell alerts since we already have the machine learning indicator providing signals.

3. Relative Strength Index (RSI)

   • As a momentum oscillator, the RSI measures the speed and change of price movements. The typical range is from 0 to 100; therefore, we will adjust the upper band to 60 and the lower band to 40 for sensitivity. This adjustment allows us to catch more valid trade entries.

Step 2: Entry Conditions for Trades

Long Trades:

To enter a long position, the following conditions must be satisfied:

1. The price closes above the 200 EMA.
2. The EMA ribbon is above the 200 EMA and colored green.
3. The price pulls back into the ribbon without closing below the long-term EMA.
4. The machine learning indicator prints a blue label.
5. The RSI is above 40 before the buy signal occurs.

Once these conditions are met, you should make a long trade and set the stop loss below the recent swing low, targeting twice the risk. If the price moves in your favor to capture a quarter of the target profit, be sure to adjust the stop loss to at least the break-even point.

Short Trades:

Conversely, for short trades, these are the requirements:

1. The price and the EMA ribbon fall below the 200 EMA.
2. The EMA ribbon colors red.
3. The price pulls back into the ribbon without exceeding the 200 EMA.
4. The RSI should be over 60 during the pullback.
5. The machine learning indicator must confirm with a sell signal.

As with long trades, set your stop loss above the recent swing high while targeting twice the risk. Move the stop loss to break-even once a quarter of your target profit is reached.

Step 3: Backtesting Results

The strategy was backtested with an initial account balance of $ 100, resulting in an impressive total of $ 19,527 after 100 trades. While the win rate may not be the highest compared to other strategies, it highlights the potential of higher risk for substantial reward. This strategy employs a 5% risk per trade, which is merged with a high reward, possibly matching the goals of those looking to quickly grow a small account.

Important Notes

• This strategy involves higher risks than traditional methods. It is crucial to conduct forward testing in a paper account before applying real capital.
• Adjusting the risk per trade is a personal decision, and traders should tailor their strategies based on their risk tolerance.

In conclusion, while this trading strategy showcases an impressive growth potential using advanced tools and techniques, always remember the importance of testing and risk management in trading.

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Keywords

• Trading Strategy
• Machine Learning
• K-N Based Strategy
• EMA Ribbon
• Relative Strength Index (RSI)
• Long Trade
• Short Trade
• Backtesting
• Ethereum
• TradingView

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FAQ

Q: What is the goal of the trading strategy?
A: The strategy aims to turn an initial investment of $ 100 into $ 10,000 by utilizing specific trading indicators and conditions.

Q: Which indicators are used in this strategy?
A: The strategy employs the Machine Learning K-N Based Strategy, EMA ribbon, and Relative Strength Index (RSI).

Q: How are long and short trades executed?
A: Long trades require the price to be above the 200 EMA, while short trades involve the price falling below it. Specific conditions related to the EMA ribbon, RSI, and machine learning indicators must also be met before entering trades.

Q: What was the outcome after backtesting the strategy?
A: The strategy was backtested and resulted in a total account balance of $ 19,527 after 100 trades.

Q: What is the recommended risk per trade?
A: The strategy involves a higher risk of 5% per trade, appropriate for those looking to grow a small account quickly. However, traders should assess their risk tolerance before applying this strategy.