You know, I recently had a wild idea: what if I asked chatgpt to whip up a trading strategy? Specifically, one that could take a modest $100 and potentially turn it into$$100 and potentially turn it into $10,000, and fast . So, I went ahead and popped the question. At first, it gave me the usual advice – focus on volatile assets, use technical analysis, be disciplined . It was all true, of course, but honestly, nothing really specific .

That’s where things got interesting. I realized I needed to be more precise with my query . I asked it to craft a strategy using a trending AI-based TradingView indicator called machine learning . I also made sure to emphasize the goal: growing that $100 into a much larger sum as quickly as possible . And get this, it actually delivered! It provided a detailed strategy . Now, it wasn’t perfect right out of the box, you know? I had to tweak it a bit to get it just right . But that initial conversation with the AI sparked this whole journey, leading to the setup we’re about to dive into .

So, let’s talk about the secret sauce behind this strategy, shall we? It all hinges on three fantastic, and best of all, free tools available on TradingView . First up is the machine learning knn based strategy . This indicator is pretty cool because it looks at historical market data and tries to predict where prices are heading based on past patterns . knn, which stands for K-Nearest Neighbors, is the algorithm doing the heavy lifting, classifying whether a price is likely to go up or down by checking out similar past data points . It analyzes things like moving averages and the Relative Strength Index to make its predictions .

What’s great about this one is that it doesn’t repaint, meaning the signals stay put once a candle closes, giving you valid trade signals . It’s also super easy to read, simply printing blue labels for buys and pink ones for sells . The opacity of these labels even gives you a hint about the signal’s strength . But here’s the thing, you can’t rely on this indicator alone; it can give false signals . That’s where our next indicator comes in, the ema ribbon . This ribbon is made up of multiple Exponential Moving Averages (EMAs) and helps us see the direction and strength of a trend . It’s crucial for filtering out those shaky signals and finding potential entries .

Finally, to really confirm our trades, chatgpt suggested adding the Relative Strength Index (rsi) as a secondary check . As you probably know, the rsi measures the momentum of price changes, showing whether an asset is overbought (above 70) or oversold (below 30) . For this strategy, we actually make the rsi a bit more sensitive to get more valid entries . We do this by adjusting the upper band to 60 and the lower band to 40 . Combining these three indicators gives us a powerful setup, you know?

Okay, so you’ve got the indicators set up. Now, let’s get into the nitty-gritty: how do you actually put this strategy into action? For a long trade, you need a few things to line up . First off, the price has to close above the 200 EMA, and the ema ribbon itself needs to be above that 200 EMA and look green . Then, you want to see the price dip back into the ribbon, but without closing below the long-term EMA . After all that, the machine learning indicator has to print a blue buy label . And here’s a key part: the rsi must have been oversold before that buy signal appeared . Once all these conditions are met, you can jump into a long trade .

Now, about managing that trade. You’ll set your stop loss just below the most recent swing low . And for your profit target, you’re aiming for 2 times the amount you’re risking . Here’s a clever trick: once your trade is showing a quarter of your target profit, you move your stop loss to your breakeven price . So, if you’re risking 5% to make 10%, and you’re up 2.5%, you secure that trade by moving your stop . For short trades, you just do the opposite . You wait for the price and the ribbon to drop below the 200 EMA, and the ribbon turns red . The price should pull back into the ribbon without closing above the 200 EMA .

During that pullback for a short trade, the rsi needs to become overbought . Then, and only then, you wait for the machine learning indicator to give you the final sell confirmation . There’s a small but important note here: don’t enter a short trade if the rsi turned oversold at the same time the sell signal appeared . You open that short trade only when all the rules are strictly followed . For short trades, the stop loss goes above the recent swing high, and your target is again 2 times the risk . And just like with longs, you move your stop to breakeven once you’ve hit a quarter of your profit target . It sounds like a few steps, but following these rules is key to the strategy, you know?

Alright, the moment of truth! After going through the setup and the rules, you’re probably wondering, “Did it actually work?” I ran a backtest with this strategy on Ethereum’s 3-minute timeframe . Starting with that initial $100 balance, get this: after 100 trades, the account balance jumped to a staggering$$100 balance, get this: after 100 trades, the account balance jumped to a staggering $19,527 ! It’s pretty incredible, right?

What’s interesting is that the win rate for this strategy wasn’t even the highest compared to others I’ve tested . So, how did it manage such a massive profit percentage? Here’s the counterintuitive bit: this strategy involves a slightly higher risk per trade than you might usually see . Instead of the typical 2%, the risk per trade was set at 5% . Now, I know what you might be thinking – higher risk means higher drawdowns, and that’s true . But it also opens the door for significantly higher rewards .

I’m definitely not saying you should risk 5% on every trade, especially if you have a larger account . However, if your main goal is to rapidly grow a small trading account, this level of risk might be suitable . The key takeaway here isn’t just the huge profit number; it’s understanding how risk management, even with a slightly aggressive approach for a small account, plays a crucial role. But, and I can’t stress this enough, always test the strategy forward on a paper account before putting real money on the line . That initial backtest result is certainly eye-opening, don’t you think?