I Built a Trading Bot with ChatGPT

Summary

In this video, Siraj showcases his trading bot, 'GPT Trader,' built with the help of ChatGPT. He demonstrates how the bot uses machine learning techniques, like neural networks and reinforcement learning, to make stock predictions using test data and the Alpaca API. The bot executes trades based on these predictions, aiming to maximize profits. Siraj also explains the setup process, including code implementation and deployment. After testing the bot for 24 hours with a $2000 investment, he reveals a profit of 1.62%. The video is a tutorial on creating and deploying an AI-powered trading bot.

Takeaways

• 🤖 The video introduces 'GPT Trader', a trading bot built with the help of Chat GPT for stock trading using machine learning techniques.
• 📈 The bot uses test data for paper trading and makes predictions on stocks like SPY and Nvidia, aiming to make profit with a $2000 investment.
• 🔮 The video demonstrates the process of selecting machine learning techniques for stock prediction, starting with neural networks as the base.
• 💡 Chat GPT provides a Python code example for a neural network using scikit-learn and Keras to predict Yahoo stock prices, emphasizing the importance of data sources.
• 🛠 The script shows troubleshooting the initial code by addressing missing dependencies and suggesting the installation of TensorFlow.
• 📊 The Alpaca trading API is introduced as a source for real-time stock data, which is crucial for the bot's live trading functionality.
• 📝 The video outlines the process of signing up for Alpaca, obtaining API keys, and integrating them into the trading bot for fetching real-time data.
• 🔬 Advanced neural network techniques such as Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs) are discussed for improving predictions.
• 🤹‍♂️ The integration of reinforcement learning with neural networks, specifically Proximal Policy Optimization (PPO), is highlighted for making trading decisions.
• 📚 The finrl library is recommended for implementing deep reinforcement learning on stock data, providing an example of how to set up the environment and agent.
• 🌐 The final deployment of the trading bot involves using a Cron job in a Flask API to run the trading bot daily on a web app, making trades based on the bot's predictions.

Q & A

What is the name of the trading bot built by Siraj and Chat GPT?

The trading bot built by Siraj and Chat GPT is called GPT Trader.

What is the purpose of the GPT Trader bot?

The GPT Trader bot is designed to make predictions on different stocks using machine learning techniques and to execute trades based on these predictions.

What machine learning techniques does the video mention for stock prediction?

The video mentions several techniques including random forests, XGBoost, time series analysis, and neural networks, with a focus on deep learning.

What is the role of the Alpaca API in the GPT Trader bot?

The Alpaca API is used to get real-time stock data for the GPT Trader bot to make informed trading decisions.

How does the video demonstrate the use of neural networks for stock prediction?

The video shows a Python code example provided by Chat GPT, which uses the scikit-learn library with Keras on top to create a neural network model for predicting Yahoo stock prices.

What is the significance of the requirements.txt file mentioned in the video?

The requirements.txt file lists all the necessary dependencies needed to run the Python code for the GPT Trader bot, ensuring that all required libraries are installed.

What is the role of the Sharpe ratio in the GPT Trader bot's trading strategy?

The Sharpe ratio is used as a measure to determine the bot's trading actions. If the Sharpe ratio is above a certain threshold, the bot will buy the stock; if it's below another threshold, it will sell.

What advanced neural network techniques does the video suggest for improving the GPT Trader bot?

The video suggests using deep reinforcement learning techniques, such as Proximal Policy Optimization (PPO), to improve the bot's trading strategy.

How does the video address the issue of getting legitimate stock data for the GPT Trader bot?

The video suggests using the Alpaca trading API to obtain real-time stock data, which is then used to train the neural network model for the GPT Trader bot.

What is the final outcome of the GPT Trader bot's live trading after 24 hours?

After 24 hours of live trading with an initial investment of over two thousand dollars, the GPT Trader bot made four trades and achieved a total profit of 1.62 dollars.

How does the video describe the process of deploying the GPT Trader bot as a web app?

The video describes deploying the GPT Trader bot as a web app using a Cron job in a Flask API, which runs a Google Colab notebook once a day to execute trades based on the bot's predictions.

What is the significance of the threshold values for the Sharpe ratio in the GPT Trader bot's decision-making process?

The threshold values for the Sharpe ratio determine the bot's buying and selling actions. A Sharpe ratio above the upper threshold (e.g., 0.4) triggers a buy, while a ratio below the lower threshold (e.g., 0.2) triggers a sell.

What does the video suggest as a way to automate the GPT Trader bot's daily operation?

The video suggests using a Cron job to automate the daily operation of the GPT Trader bot, running a specified Python Flask API at a set time each day.

How does the video handle the issue of module installation when the initial Python code does not work?

The video acknowledges the issue and suggests installing the missing module, TensorFlow, manually to resolve the 'no module named tensorflow' error.

What is the role of the finrl library in the GPT Trader bot's implementation?

The finrl library is used to demonstrate how to apply deep reinforcement learning to real-time financial data for making predictions, which is then integrated into the GPT Trader bot.

What is the video's stance on the reliability of Chat GPT as a coding assistant?

The video recognizes that Chat GPT is not a perfect coder but appreciates it for providing a scaffolding or starting point for building the GPT Trader bot.

What does the video imply about the importance of testing trading bots with paper trading before live trading?

The video implies that testing with paper trading is crucial for understanding the bot's performance and making necessary adjustments before risking real money in live trading.

What is the video's approach to combining machine learning with domain-specific knowledge in the GPT Trader bot?

The video's approach involves integrating advanced machine learning techniques, such as deep reinforcement learning, with domain-specific knowledge of financial markets to enhance the GPT Trader bot's performance.

Outlines

00:00

🤖 Building a Trading Bot with Chat GPT

In this paragraph, the creator introduces a trading bot named 'GPT Trader' that they developed in collaboration with Chat GPT. The bot is designed to make predictions on stock trading using machine learning techniques. The creator demonstrates the bot's capabilities using test data with platforms like the alpaca dashboard and discusses the process of integrating the bot with live trading. They also engage Chat GPT to list the top ten machine learning techniques for stock prediction, highlighting neural networks as the most popular choice. The creator then proceeds to ask Chat GPT for Python code examples to predict stock prices using neural networks, emphasizing the bot's ability to learn and adapt in the context of stock trading.

05:01

📈 Implementing Neural Networks for Stock Prediction

The creator delves into the specifics of using neural networks for stock prediction, starting with obtaining a Python code example from Chat GPT. They discuss the process of setting up the environment, including installing necessary libraries and handling potential issues like missing dependencies. The paragraph covers the integration of real-time stock data using the Alpaca trading API, which provides a more legitimate data source than the initially assumed CSV file. The creator also explores advanced neural network techniques, such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), gated recurrent units (GRUs), and transformer networks, ultimately deciding to use deep reinforcement learning for the trading bot's strategy.

10:02

🧠 Advanced Techniques: Proximal Policy Optimization (PPO)

This section focuses on the advanced neural network technique of Proximal Policy Optimization (PPO), which the creator intends to use for their trading bot. Chat GPT provides an explanation of PPO suitable for a five-year-old, illustrating it as finding the optimal balance between different outcomes. The creator then seeks Python code examples for implementing PPO and discovers the 'stable-baselines' library by OpenAI. They decide to use the 'finrl' library, which is tailored for financial reinforcement learning, to integrate PPO with stock trading. The paragraph outlines the process of setting up a simulated environment, training the PPO agent, and using the Alpaca API for real trades based on the agent's predictions.

15:03

🔄 Automating Trades with a Daily Cron Job

The final paragraph details the deployment of the AI trading bot as a web app that operates on a daily schedule, facilitated by a Cron job. The creator explains how the bot uses reinforcement learning to decide whether to buy or sell stocks based on the sharp ratio threshold. They describe the process of setting up a Cron job in a Flask API to run a Google Colab notebook at a specified time daily, which in turn triggers the bot to make trades via the Alpaca API. The creator also shares their experience after 24 hours of live trading with the bot, reporting a profit of $1.62 from four trades, and expresses gratitude to the audience for watching the video.

Keywords

💡Chat GPT

Chat GPT refers to a conversational AI system that can interact with users in a human-like manner. In the video's context, it is used to create a trading bot, indicating its capability to assist in complex tasks such as stock trading algorithms. The script mentions Chat GPT's role in building the 'GPT Trader' bot and its ability to remember context, which is crucial for the development and execution of the trading strategies discussed.

💡Trading Bot

A trading bot is an automated software application that trades assets such as stocks, cryptocurrencies, or foreign exchange on behalf of a user. In the video, the creator collaborates with Chat GPT to build a trading bot named 'GPT Trader,' which uses machine learning techniques to predict stock prices and execute trades, highlighting the integration of AI with financial trading strategies.

💡Machine Learning Techniques

Machine learning techniques are methods used in the field of artificial intelligence to give systems the ability to learn from data and improve over time. The video script lists several techniques such as random forests, XGBoost, and neural networks, which are considered for stock prediction. These techniques are central to the video's theme, as they form the basis of the trading bot's predictive capabilities.

💡Neural Networks

Neural networks are a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. They are a subset of machine learning and are particularly popular for their ability to handle complex data. In the script, neural networks are chosen as the foundation for the trading bot's predictive model, emphasizing their significance in modern AI applications.

💡Scikit-learn

Scikit-learn is an open-source machine learning library for the Python programming language. It is widely used for various statistical modeling and machine learning tasks. In the video, scikit-learn is mentioned as the library used in conjunction with Keras to create a neural network model for predicting stock prices, showcasing its role in implementing machine learning algorithms.

💡Keras

Keras is an open-source software library that provides a Python interface for artificial neural networks. It is known for being user-friendly and allowing for easy and fast prototyping. The script refers to Keras as being used on top of the scikit-learn library to facilitate the creation of the neural network for stock price prediction, illustrating its utility in deep learning applications.

💡Requirements.txt

A 'requirements.txt' file is a list of dependencies required by a Python project, which can be installed with a single command using the 'pip' package manager. In the video, the script mentions creating a 'requirements.txt' file to list all the necessary libraries for the trading bot, such as tensorflow, which is essential for setting up the environment to run the neural network model.

💡Alpaca Trading API

The Alpaca Trading API is a platform that provides commission-free algorithmic trading access to the US stock market. It is mentioned in the script as the chosen API for obtaining real-time stock data for the trading bot. The use of Alpaca API highlights the video's focus on utilizing real-time data for making informed trading decisions.

💡Deep Reinforcement Learning

Deep reinforcement learning is a subfield of machine learning where an agent learns to make decisions by taking actions in an environment to maximize a reward. In the video, the concept of deep reinforcement learning is adopted to enhance the trading bot's decision-making process, combining neural networks with reinforcement learning techniques to improve stock trading strategies.

💡PPO (Proximal Policy Optimization)

PPO, or Proximal Policy Optimization, is a policy gradient method used in reinforcement learning to optimize the policy of an agent. It is highlighted in the script as an advanced neural network technique chosen for the trading bot. The video explains PPO as finding an optimal balance in decision-making, which is crucial for the bot's ability to make profitable trades.

💡Cron Job

A cron job is a time-based job scheduler in Unix-like operating systems that executes commands at specified intervals. In the video, the script discusses using a cron job to automate the running of the trading bot once a day, demonstrating the application of automation in maintaining and operating the AI-driven trading system.

💡Collab Notebook

A collab notebook, typically referring to Google Colab, is a cloud-based Jupyter notebook that allows users to write and execute Python code in a collaborative environment. The script mentions deploying the AI trading bot in a collab notebook, which is used to run the bot's code daily, emphasizing the use of cloud services in AI and machine learning workflows.

💡Sharpe Ratio

The Sharpe ratio is a measure of the performance of an investment compared to a risk-free asset, after adjusting for its risk. It is used to determine the consistency of an investment's returns. In the video, the Sharpe ratio is utilized as a threshold to decide whether to buy or sell stocks, indicating its importance in evaluating the risk-adjusted performance of the trading bot's strategy.

Highlights

Introduction of GPT Trader, a trading bot built with chat GPT for stock trading.

Use of test data for making predictions on stocks like SPY and Nvidia with the help of machine learning techniques.

Chat GPT's role in assisting with the development of the trading bot and its capabilities for paper trading.

Discussion on the best machine learning techniques for stock prediction, including neural networks and deep learning.

Demonstration of using Python and scikit-learn with Keras to create a neural network model for stock price prediction.

Challenges faced with the initial code, including the need for a legitimate CSV file for stock data.

Request for a requirements.txt file to install necessary dependencies for the trading bot.

Encountering issues with module installation and the need for a better data source.

Introduction of the Alpaca trading API as a zero-commission method for obtaining stock data.

Explanation of how to sign up for Alpaca and use its API keys for trading.

Integration of Alpaca API with Python to fetch real-time stock data for the trading bot.

Discussion on advanced neural network techniques like recurrent neural networks, LSTM, GRU, and transformer networks.

Introduction of deep reinforcement learning as a cutting-edge technique for the trading bot.

Explanation of Proximal Policy Optimization (PPO) as a reinforcement learning technique.

Use of the finrl library for implementing deep reinforcement learning on financial data.

Setting up a web app to run the trading bot daily using a Cron job and Flask in Python.

Deployment of the trading bot to Versal for automated daily trading based on reinforcement learning predictions.

Outcome of the trading bot's performance after 24 hours, making a profit with real money investment.

Transcripts

hello world it's siraj and I gave chat

GPT two thousand dollars to trade with

it's a trading bot that I built with

chat GPT and I want to show it to you in

this video GPT Trader that's what I'm

calling it once we log into this app we

can see that I've got the alpaca

dashboard and I'll explain this to you

later and it's using test data so using

test data I was able to make predictions

on different stocks including spy and

Nvidia and chat GPT helped me build this

bot now that's just using paper trading

let me show you how to use this with the

live trading and that's at the end of

the video did I make money with my two

thousand dollar investment or did I lose

money find out at the end so let's start

with our first question for chat GPT

what are the best techniques to be using

here so let's ask it that what are 10 of

the best machine learning techniques for

stock prediction let's see what it gives

us here

so it's going to list some techniques

and again we're Noob so we don't know

what all these things are you know I can

say you know random forests are the

ability to take decision trees which are

if then statements and then use them in

an ensemble or group method I can say XG

boost is a way of improving predictions

on the popular website kaggle I can say

that time series analysis that's a

technique to make predictions based on

past data that is spread out across the

time domain but the most popular one are

neural networks that's what everything

is based off of these days deep learning

so let's pick number six let's ask

chatgpt another question because

remember chat EBT unless unlike other

AIS is able to remember the context so

let's ask it the next question show me a

python web example of using a of using

that to predict the price of Yahoo stock

let's just pick a stock

and it's immediately going to do that it

knows that this is referring to a neural

network it knows that number six is

neural network and it knows to use the

Deep learning library scikit-learn with

Keras on top to do this now what's

happening here it's going to actually

tell us what's happening at the end but

I'll give you a little brief uh preview

of what's happening it's using the

scikit-learn library to make a

statistical model called a neural

network we can define a neural network

in seven words input times weight at a

bias activate it's a series of math

operations to make predictions Based on

data it's using the Yahoo data set of

past stock prices to do that now where

is it getting the CSV file the CSV file

is not coming from anywhere it's just

assuming that we have this data so what

we're going to have to do is we're going

to try to compile this in a in a code

window and see what this does for us and

as you can see it's telling us what this

code does so let's take all this code

we're going to hit copy okay and we're

going to go to our terminal window

and in our terminal window we're going

to

create a new folder

and in that folder we're going to create

a new file that's a python file we'll

call it stockprediction.pi

in stock prediction.pi we're going to

paste in the exact code that chat gbt

told us to and we're going to save it

and run it with the python command now

what do you think is going to happen any

guesses instead of just installing these

why don't we ask chatgpt for the

requirements file to install these what

are the requirements dot txt5 what does

the

requirements.txt file look like

look like for the previous code so all

those dependencies it's going to give it

to us look like for the previous code

and it's going to give us a

requirements.txt file and then all we

have to do is do pip install

requirements.txt and then we have all of

our dependencies right there so it's

going to tell us exactly how to do that

and we can just copy that create a new

requirements.txt and once we've done

that we can run that as a pip installs

pip install we're going to do it

recursively so all of them are going to

be installed no module named tensorflow

so I guess it's not perfect what we're

learning is that chat gbt clearly isn't

a perfect coder but it's giving us a

scaffolding here so we've got to install

tensorflow and there's a way to install

tensorflow but already off the bat we

know that this isn't going to work

because the data isn't legit so let's

ask chat CPT for a better data source

shall we and then we'll go back to this

code remember we're working with this

thing as a co-founder so our next step

is to say

is there some way to get the data what's

a good way to do this now I don't want

to pay for stock data I don't know about

you so the way to do that is to ask it

is there a zero commission that means a

way to trade stocks without paying money

is there a zero commission let's say

developer first sound like a ad Right

Now API for stocks and I also like

crypto so I'll say and crypto and let's

see what it gives us hopefully it gives

us one because I don't want to be too

complicated here and we're going to use

this API to get real-time data from the

web today of whatever stock we want and

it looks it says the alpaca trading API

so that's the one we're going to use

because chatgpt knows best doesn't it so

let's go to alpaca and see what how to

sign up for that now clearly I have

signed up for this and I've already

started making trades on the test

Network I've made a bunch of spy trades

and I made forty four dollars of profit

using you know 80 about 80 000 of fake

investment great job Suraj but better

job chat GPT I should be saying so once

we see that this this is cool we can

sign up for alpaca assume we've signed

up for alpaca and we've got the API keys

and that's what they're going to look

like right here they're going to look

like these API keys that you see here so

once we have the API keys for alpaca

we're going to want to get an example of

using it in Python right so we could go

through the docs and do that but better

than going through the docs let's just

ask chat GPT so thanks for showing me

that API show me an example of it

in Python and we're going to assume that

it has a python SDK for it oh great it

does the alpaca trading API in Python

perfect now remember the previous

example it showed us it gave us bad

stock data right it wasn't legit this is

going to give us real-time stock data

and as you can see by the parameters we

can Define what the start and end time

for this data is going to be so what

type of trading bot do I want I want a

trading bot that trades every 24 hours

it makes one trade every 24 hours based

on one data point and that data point is

a sharp ratio and it's computed using a

very modern technique but which

technique should we be using and I'll

tell you which technique which we should

be using in a second by asking chatgpt

but as you can see here it's given us a

simple example of using the alpaca API

to get a data feed so let's take this

data feed go back to our code and we're

going to add this to the top of our code

that means that first we're going to get

the data from alpaca then we're going to

use that scikit learn library with Keras

on top to train a very simple model a

statistical machine learning model on

that data but that's not going to be

enough right because if that it was that

easy everybody would be doing it so

let's first take this go back to our

code here we'll go to the stock

prediction code

and

we're going to just paste that in at the

bottom right under underneath what we

already have and we'll make sure to add

the import at the top so it looks really

cool we'll add our API key as well

remember we have our API because we

signed up for alpaca so we'll go back to

the dashboard we'll get that API key

there it is for the paper API and that

means that it's the one that's not

trading with real money that's what we

want right now as we're noobs we'll go

back to the code and replace that and

there we go now we have a python script

that's pulling real data

from the web we don't want to just use a

simple neural network we want to use

something awesome something's Cutting

Edge right what are the you know real

pros using here so what are we going to

ask it let's literally ask it what are

the most

Advanced neural network techniques to

use here

just what's going on Chachi BT what

should we be doing and it's going to

tell us all the different types of

neural networks to be using now in the

past five years we've seen so much

research and development in neural

network technology starting with

recurrent neural network which is what

you're seeing up here this is the idea

of taking the input at every previous

time step and putting it back into the

network at each recurring time step

hence the word recurrence that offered a

lot of progress in terms of predictions

for time series data so did variants of

recurrent networks like long short-term

memory networks so did gated recurrent

units Gru networks so when we take the

idea of a transformer Network and we

combine it with reinforcement learning

what do we get

that's right deep reinforcement learning

and that's what we want to use so we're

going to take this idea of reinforcement

learning and this idea of a neural

network that it also has given us and

we're going to combine it with our own

domain knowledge of this idea of a

domain specific reinforcement learning

technique called Deep reinforcement

learning so what does that look like in

Python right that's a very abstract

example and the way to

look at that in Python is to say well

okay what are some of the most popular

reinforcement learning techniques

and it's going to tell us all of them

it's going to start with uh proximal

policy optimization it's going to or Q

learning actually and then it's going to

move to proximal policy optimization and

some other one sarsa so we don't know

what a lot of these acronyms are and a

lot of reinforcement learning is

essentially a series of acronyms now

policy gradients as you see here is a

variant of proximal policy optimization

which I know is a really good technique

just from the literature that I've been

looking at on the internet so there we

go number six so we're going to take

number six which looks totally

hard to understand unless you're a total

expert at this and that's the one we're

going to pick so we're going to pick

this super Advanced technique that chat

topt just gave us and we're going to say

okay please explain PPO please explain

number six to me like I'm a

five-year-old

oh and show me some python code for it

okay so that's it okay perfect so it's

going to tell us it's a way to teach a

computer to make decisions like a human

and so just with that I get a very basic

idea of what's Happening here it is

trying to find the average between two

various maximal points it's trying to

find the optimal area between two ranges

in two different directions of whatever

your policy is in this case the policy

is going to be to find the right stock

picks at the right time so it's already

given us an example using stable

Baseline stable baselines is open ai's

own library for reinforcement learning

and it's using in three lines of code

the proximal policy algorithm on a

environment that is has nothing to do

with stock prices so we want to replace

it with a stock price environment so

let's go to GitHub and we'll type in

stable baselines we're going to type in

not just stable Baseline but open AI

because openai made stable baselines and

we'll type in financial just to see if

we can find anything that's Financial

related and under code and it turns out

that if we just type in that the first

result is going to be this fin RL

Library by the AI for finance Foundation

very cool stuff under examples they have

a an IPython notebook that shows how to

use deep reinforcement learning on

real-time data to make predictions so

let's go to that demo by using what

openai told us it's what chat GPT told

us it said use reinforcement learning

use proximal policy optimization and

here's a python example but there's not

an exact way to do that using the

existing codes we're going to take a

little detour here and use this library

right here finrl to make those

predictions so let's open that up in our

own collab notebook and as you can see

I've opened it up right here now what

this is going to do let's go through

this step by step the first thing it's

going to do is going to install this

repository into the browser after it's

installed its dependencies and you can

see a lot of code here then it's going

to create some folders to save all of

this data as it trains on

once it's pulled the data now how is it

pulling the data what it's using right

here is the Yahoo downloader so it's

pulling data in re from whatever date

ranges we choose but we're going to

switch that with the alpaca API once

it's pulled that data it's going to show

it in terms of all of the variables that

are a part of that data and it's going

to find 30 days of stock data for all 30

of these tickers from Apple all the way

down to the Dow and then it's going to

add what are called technical indicators

to those uh rows so all of those single

stocks are also going to have these very

popular technical indicators which are

known in the community

once it has their technical indicators

then we're going to create a Markov

decision process what is a Markov

decision process this is a this is an

idea in reinforcement learning where you

have an agent in an environment and it

takes a step at every given time step a

Markov decision process is a way to

frame this process in such a way that it

is mathematically possible to define a

policy that means how should this agent

be acting at every time step and so what

is the policy for us the agent first of

all is going to be this AI that is a

neural network specifically a proximal

policy optimization neural network and

the environment is the financial Market

what this library is going to do is it's

going to pull that real-time data into a

simulated environment train this deep

reinforcement learning agent in that

simulated environment and then use the

alpaca API to make real trades based on

the output of that every single day I

will then deploy that to the web and

it's going to run every single day for

me okay so that's what's happening here

I'm defining the variables for this deep

reinforcement learning Ensemble agent so

it's actually running three different

strategies a2c PPO and ddpg now remember

chat GPT showed us what those

reinforcement learning strategies were

okay so once it's trained and you can

see all this training data it's going to

show a back test and what a back test is

is it's a way for an automated system

without biases to make predictions based

on previous data now you and I we can't

do that because we're biased we would

just pick if we already know what it's

going to be what the price could be but

as you can see here on this test that I

ran earlier

you can see that the green line is the

actual values and the gray line is the

return that our bot made so that means

that on that data it made it returned

better than the market so now it's up to

us do we want to use that to buy and

it's always going to change right based

on every day if if a given day is two

percent profit do I want to buy or do I

want to sell what I'm going to do is I'm

going to Define a threshold and I'm

going to say if the sharp ratio is above

say 0.4 in this case I'm just randomly

saying a number go ahead and buy Nvidia

stock if it's not

sell and that's it and I want to let

that run for five days and it's just

going to run on a web app so that's the

idea do we have anything else to ask

chat GPT we have this thing uh oh yeah

we need to add the alpaca API to it and

you can see it right here so this is

going to make trades based on the bot

okay so let's connect the bot that's

already trained to the alpaca API so

with this single function it can make

that trade with one function

programmatically given the secret and

the public key

so what is our web app even doing well

it's running a collab notebook once a

day from start to finish at a given

interval that's it and then it's going

to make a trade if that reinforcement

learning library says to make a trade so

how do I get it to do that well we need

to run what's called a Cron job so let's

ask trap GPT what that is and then how

to do that so let's say I want to run

this AI trading bot once a day

the way to do that

is with a Cron job apparently because

Raj said so right is with a Cron job

what is a Cron job and write me one in

flask in Python flask because I want

this to be a flask API in Python flask

to deploy to versal which is my favorite

deployment platform

um

and it runs once a day it just runs a

Google collab once a day

that's it

a Cron job is a scheduled task that runs

automatically at specified intervals on

a Linux or Unix based system perfect

that's exactly what we wanted so it's

telling us how to run this with the

python code so

with this single class file of code we

can ask versal to run this collab

notebook once a day at every you know at

8 AM let's say 8 A.M

and it's going to buy the stock and then

it's gonna if it's good enough right if

the AI agent if the PPO agent has a

predicts a sharp ratio that is above my

threshold then it will buy else it will

hold and if it's below a certain ratio

it will sell let's say 0.2 so 0.4 is the

upper threshold and 0.2 is a minimum

threshold so that's the script and we're

going to deploy it to Virtual with

terminal so I'm going to take that go

back to my code that was hosted locally

and I'm going to deploy this with my

white labeled uh Firebase

starter package here that you guys can

find on my GitHub by the way gbt Trader

make it whatever you want but it's got

Firebase and everything integrated so

that's that's how this is going to work

I'm going to take it deploy to Virtual

and right now I have deposited money

into my Live account but I'm still

waiting on that deposit so once that

deposit is complete I'm gonna let the

bot make a trade and I'm going to come

back in 24 hours and let's see how much

money it's made all right here we go

okay it's been 24 hours and after

investing over two thousand dollars it's

made four trades and made a total of

1.62 profit not bad thank you guys so

much for watching subscribe if you liked

the video hit that like button it really

helps promote the video and for now I've

got to go catch GPT myself so thanks for

watching

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