Lets use this insane new technology to develop,, an insane new technology.

First, lets detail the reasons why I prefer algo trading to discretionary..

Trading bots are effective in crypto markets for several reasons:

  1. Speed: Trading bots can execute trades much faster than a human trader could, as they are able to process large amounts of market data and execute trades within milliseconds. This speed can be especially important in fast-paced crypto markets, where prices can change rapidly.

  2. 24/7 Trading: Crypto markets never sleep, and trading bots can be programmed to run continuously, allowing them to take advantage of market conditions 24 hours a day, 7 days a week.

  3. Reduced Emotions: Trading bots are not subject to emotions like fear, greed, or excitement, which can lead to impulsive or irrational trading decisions. This can result in more consistent and profitable trades.

  4. Backtesting: Trading bots can be tested and optimized using historical market data, allowing traders to evaluate their strategies and refine their approach.

  5. Automated Decision Making: Trading bots can be programmed with a set of rules and conditions, and they will execute trades based on those conditions without the need for human intervention. This can help to remove the emotional and psychological biases that can impact human traders.

Now we need a platform to host the code and trade for us..

TradingView does a great job of exactly this! TV uses a language known as pinescript, so we must prompt Chat GPT to utilize specifically this language such that it can be loaded into TradingView.

Here is the prompt I provided!

and the output I was provided:

study("1 Hour Bitcoin Trading Bot")

// Define the strategy variables

fastLength = 10

slowLength = 20

threshold = 0.02

minProfit = 0.05

minLoss = -0.03

long = false

short = false

// Calculate the fast and slow moving averages

fastMA = sma(close, fastLength)

slowMA = sma(close, slowLength)

// Check if the fast moving average crosses above or below the slow moving average

if cross(fastMA, slowMA)

if fastMA > slowMA

long := true

short := false

else

long := false

short := true

// If long is true, enter a long position

if long

strategy.entry("Long", strategy.long, stop = strategy.position_avg_price * (1 - threshold))

// If short is true, enter a short position

if short

strategy.entry("Short", strategy.short, stop = strategy.position_avg_price * (1 + threshold))

// Exit the current position if the fast moving average crosses back across the slow moving average

if (long or short) and cross(slowMA, fastMA)

strategy.close("Closing position")

// Exit the current position if the profit or loss reaches the minimum target

if strategy.profit >= strategy.position_size * minProfit

strategy.close("Min profit reached")

if strategy.profit <= strategy.position_size * minLoss

strategy.close("Min loss reached")

Now, lets load this into TradingView and see how it does. Here is how:

Open > New Strategy > Paste the Output!

One can then hit "Strategy Tester" to see how the code performs!

Although the output it provides is very basic, the framework is solid. Utilizing montecarlo engines and other tools, it can be developed further!