Recently, I sought advice from ChatGPT to develop a trading strategy to turn $100 into $10,000 as quickly as possible. While the initial suggestions were generic, I decided to be more specific and requested a strategy using an AI-based trading view indicator called Machine Learning KNN. This article will provide a detailed step-by-step guide to setting up and testing the strategy to determine its effectiveness.
Step 1: Opening Charts and Adding Indicators Before testing the strategy, we need to add the necessary indicators to the trading chart. The strategy includes three free trading tools, which we will add one by one.
To begin, open a trading chart on your preferred trading platform or software. This could be a platform like TradingView or any other charting software that supports the indicators mentioned in the strategy.
Once you have opened the chart, locate the option to add indicators or tools to your chart. This is typically represented by an icon that resembles a chart or a plus sign (+). Click on this icon to access the indicator library.
Step 2: Adding the Machine Learning KNN Indicator The first indicator we will add is the Machine learning-based KNN strategy. This indicator analyzes historical market data and predicts future price movements based on patterns. It utilizes the K-nearest neighbors (KNN) classification algorithm to determine whether a stock price is likely to go up or down.
In the indicator library, search for the Machine Learning KNN indicator. This indicator may have been created by a specific developer or author, so it's important to know its exact name or the name of the author who developed it.
Once you have found the Machine Learning KNN indicator, click on it to add it to your chart. Depending on the platform or software you are using, there may be options to customize the settings or parameters of the indicator. Take a moment to familiarize yourself with these options and adjust them as necessary based on your preferences or the default settings recommended for the strategy.
The Machine Learning KNN indicator will now be displayed on your chart, analyzing historical market data and providing predictions about future price movements based on patterns it has identified. Pay attention to the signals or labels generated by the indicator, as these will guide your trading decisions.
Step 3: Understanding the EMA Ribbon Indicator To filter out false signals, we will add the EMA Ribbon indicator by Dominic or Selecti. The Exponential Moving Average (EMA) Ribbon consists of multiple EMAs with different time periods stacked on top of each other. This tool helps identify the direction and strength of a trend in the market.
Step 4: Adding Relative Strength Index (RSI) To further confirm valid trade entries, we will use the Relative Strength Index (RSI). The RSI measures the strength of a security's price action and ranges from 0 to 100. We will make the RSI more sensitive by adjusting the upper and lower bands to 60 and 40, respectively.
Step 5: Entry Conditions for Long Trades To open a long trade, the following conditions must be met:
The price must close above the 200 EMA.
The EMA Ribbon must be above the 200 EMA and green.
Price must pull back into the ribbon without closing below the long-term EMA.
The Machine Learning KNN strategy must print a blue label.
The RSI must be oversold prior to the buy signal.
Step 6: Setting Stop-Loss and Profit Targets for Long Trades Once the conditions for a long trade are met, set the stop loss below the recent swing low and target a profit of two times the risk. Once the trade has reached 1/4 of the profit target, adjust the stop loss to the break-even price.
Step 7: Entry Conditions for Short Trades To open a short trade, the following conditions must be met:
Price and the EMA The ribbon must fall below the 200 EMA, and the ribbon must turn red.
Price must pull back into the ribbon without closing above the 200 EMA.
The RSI must become overbought during the pullback.
Machine Learning KNN must provide a sell signal, excluding cases when the RSI is oversold.
Step 8: Setting Stop-Loss and Profit Targets for Short Trades For short trades, set the stop loss above the recent swing high and target a profit of two times the risk. Move the stop loss to the break-even point once 1/4 of the profit is made.
Step 9: Backtesting and Results With the setup complete, proceed to backtest the strategy using the price of Ethereum on a 3-minute timeframe. Execute the strategy 100 times and record the results. In this specific case, the starting account balance of $100 increased to $19,527 after 100 trades.
Requesting Specific Advice from ChatGPT: Initially, ask ChatGPT to provide a trading strategy to turn $100 into $10,000 as quickly as possible. You will receive some general tips, such as focusing on highly volatile assets, using technical analysis, and maintaining disciplined trading practices. However, you want more specific guidance.
Step 2: Refining the Question for ChatGPT To obtain more targeted advice, you need to be more specific in your question. So ask ChatGPT to create the best strategy using an AI-based trading view indicator called Machine Learning. This indicator is very popular and viral.
Conclusion: While this strategy involves higher risk due to the 5% risk per trade, it can help grow a small account rapidly. However, it is crucial to conduct forward testing on a paper account before implementing it with real funds. Remember, risk management and thorough testing are vital aspects of successful trading.
