Dominate the Markets with ChatGPT and TradingView

Attention all traders! Are you looking for a cutting-edge way to dominate the markets and maximize your profits? Look no further than the power of ChatGPT and TradingView. By combining the advanced AI capabilities of ChatGPT with the unparalleled charting and analysis tools of TradingView, you can create custom trading strategies with ease and precision – without knowing how to code

In the following video, I show you how to leverage the power of ChatGPT and TradingView to achieve your financial goals and reach the next level of trading mastery.

The goal of the video isn’t to come up with a billion dollar strategy. If I had one, I wouldn’t share it. The goal is to empower non-programmers with the tools to be able to backtest their own strategies and subsequently revolutionize their trading game.

How to Create Trading Strategies with ChatGPT & TradingView

To create a trading strategy using ChatGPT and TradingView, you can follow these steps:

  1. Define the objective of your trading strategy: Identify the type of market you want to trade (e.g., stocks, forex, crypto), the time frame for your trades, and the type of trading (e.g., swing trading, day trading, scalping).
  2. Gather data: Collect historical price data for the markets or assets you are interested in trading. You can obtain this data from TradingView or other financial data sources.
  3. Determine the trading signals: Based on your gathered data, you can use TradingView’s scripting language (PineScript) to write code that generates signals when certain conditions are met. For example, you could create a simple moving average crossover strategy that generates a buy signal when the short-term moving average crosses above the long-term moving average.
  4. Backtest your strategy: Use TradingView’s backtesting feature to test your strategy against historical price data. This will help you evaluate the performance of your strategy and fine-tune it as necessary.
  5. Implement your strategy: Once satisfied with your backtesting results, you can implement your strategy in a live trading environment. You can code it up algorithmically or use TradingView’s “Alerts” feature.

Note: Keep in mind that creating a successful trading strategy is a complex process that requires a deep understanding of financial markets and technical analysis. Understanding the risks associated with trading and managing your trades carefully is also important.

ChatGPT Strategy Creation Walk-Through

I created a video on YouTube that went viral. Many requested me to post the code, so here it is. Make sure you watch the entire video where I explain that I’m purposely overfitting. It’s pretty easy to make a Bitcoin strategy that is immensely profitable due to obvious reasons.

Algorithmic trading is extremely difficult. Again, the point of the video wasn’t to come up with a billion-dollar strategy but to show a non-programmer how they could backtest their own strategy while having some fun along the way.

Video chapters:

  • 0:00 Introduction
  • 1:38 Intro to TradingView & Pinescript
  • 2:23 Create First Trading Strategy with ChatGPT
  • 4:29 Use ChatGPT to Modify Existing Strategies
  • 5:20 Have ChatGPT Explain Pinescript Strategies
  • 6:37 Understanding the Trading Strategy Returns
  • 7:57 Have ChatGPT Update Your Strategy’s Code
  • 9:18 Learn How to Get The Information You Need
  • 11:56 Improving Strategy Returns
  • 13:55 Adding RSI to Our Trading Strategy
  • 16:25 Checking Modified Strategy Results
  • 17:51 Optimization, AKA Overfitting
  • 18:28 Adding the ADX 21:05 Success: $1,000 to $17 Million

ChatGPT Insane Strategy Video PineScript

strategy("Supertrend Strategy", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=120,
     initial_capital=1000, margin_long=0.1)

atrPeriod = input(10, "ATR Length")
factor = input.float(3.0, "Factor", step = 0.01)

[_, direction] = ta.supertrend(factor, atrPeriod)

adxlen = input(7, title="ADX Smoothing")
dilen = input(7, title="DI Length")
dirmov(len) =>
    up = ta.change(high)
    down = -ta.change(low)
    plusDM = na(up) ? na : (up > down and up > 0 ? up : 0)
    minusDM = na(down) ? na : (down > up and down > 0 ? down : 0)
    truerange = ta.rma(, len)
    plus = fixnan(100 * ta.rma(plusDM, len) / truerange)
    minus = fixnan(100 * ta.rma(minusDM, len) / truerange)
    [plus, minus]
adx(dilen, adxlen) =>
    [plus, minus] = dirmov(dilen)
    sum = plus + minus
    adx = 100 * ta.rma(math.abs(plus - minus) / (sum == 0 ? 1 : sum), adxlen)
sig = adx(dilen, adxlen)

if ta.change(direction) < 0 and ta.rsi(close, 21) < 66 and ta.rsi(close, 3) > 80 and ta.rsi(close, 28) > 49 and sig > 20
    strategy.entry("My Long Entry Id", strategy.long)

if ta.change(direction) > 0
    strategy.close("My Long Entry Id")

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