Automating Trading Strategies with GPT Integration

Sangchul Yoon innovatively integrates GPT into automated trading strategies to enhance investment decision-making and efficiency.

### Advancements in Automated Trading: Integrating GPT for Enhanced Decision-Making
* 📈 In this video, Sangchul Yoon from TacozLabs Accounting shares his progress on an **automatic trading program** developed using Python and the Korea Investment & Securities API.
* 🤖 He has tested a new idea to refine the algorithm, aiming to **enhance profitability** and align the trading strategy with his **personal investment style**.
* 📊 Yoon discusses using GPT for investment decision-making, allowing the trading program to **send market data and receive real-time guidance**.
* 📉 He highlights a practical test where GPT analyzed **three years of stock market data** and recommended investments based on technical analysis methods like **MACD and RSI**.
* 💡 Yoon envisions daily interactions with GPT to receive **stock recommendations and trading instructions**, automating **buy, sell, or hold decisions** every ten minutes.
* 💳 He mentions the **cost-effective nature** of the OpenAI API, which he has integrated into his trading system, allowing for **real-time queries and responses**.
* 📊 Future plans include incorporating **real-time news updates** and linking a personal investment database to GPT for **more precise trading decisions**.
* 🔍 Overall, Yoon expresses excitement about the **potential of GPT in automating trading strategies**, promising to share further developments in upcoming videos.

Hello, this is Sangchul Yoon from TacozLabs Accounting. In this video, I want to share the advancements I've made in the automatic trading program I've been working on. I've tested a new idea to enhance the algorithm, and I'm excited to share this progress with you. In the next sessions, I plan to implement and test this approach further. As you might have seen in the thumbnail, up to now, I've been developing an automatic trading program using Python, leveraging the user-friendly API provided by Korea Investment & Securities. I've shown you how this setup can facilitate automatic trading. However, to improve profitability and tailor the trading to my preferences, I've realized the algorithm needs further refinement. This led me to consider the various technical analysis techniques available for day trading and how they could be adapted to the Korean stock market or my investment style. Most of you are likely working full-time and understand how challenging it is to find time

for such projects after work. This is where I thought of utilizing GPT, which has become quite accessible in recent years. I've subscribed to the OpenAI API and have been using it for tasks such as holding meetings with international clients or composing emails in English. It occurred to me that GPT could also assist in investment decision-making by enhancing the algorithm for my trading program. The idea was to use GPT for making investment decisions, which could then be communicated in real-time to the trading program. The program would send market data to GPT, which would return decision-making guidance. To implement this, I considered how to design the system. I chose to leverage the GPT API, which has been available for about a year now. Typically, we interact with GPT through a web interface by posing questions and receiving answers. However, I wanted to explore a more engaging application. For example, in one of my tests, I used publicly available libraries to download stock market

data. I uploaded a file named GPT_data_all.csv, which contained three years of price data (opening, closing, highest, lowest prices, and volume) for five companies listed on the KOSPI. I then asked GPT to analyze this data from 2021 for day trading purposes, inquiring about applicable technical analysis techniques. GPT listed several methods, such as MACD and RSI. With this information, I queried GPT on which of the five stocks it would recommend investing in immediately based on these technical analysis methods. GPT proceeded to analyze the data for all five tickers using the MACD technique, among others, and provided its recommendation on which stock seemed most promising. This approach allowed GPT to rank the five tickers based on various technical analysis criteria, using the dataset I provided. It's an innovative way to combine AI capabilities with stock market investments, showing the potential of using GPT not just for generating text but for making informed financial decisions.

And then, finally, it's a bit long, but by saying do this, it produced this result. So, there are five tickers, and it used four analytical methods to predict which ones would be effective for investment according to each method, leading to outcomes for each ticker. Then, posing the ultimate question: If you were day trading, what would you do tomorrow? It makes a decision from there, choosing ticker 5490. As you know, our country's ticker symbols are six digits, so if 5490 has only four digits, it implies the missing zeroes at the beginning. It then discusses why investing in this option could be advantageous. This made me think, essentially, I'd be conversing with it daily, asking, Hey, what's happening in the KOSPI, the Korean stock market? Give me the data, what technical analysis items are available? and so on. Then, about an hour before the market opens, tell me the top ten stocks you'd recommend.

buying at the opening. After receiving this, my automated trading program processes these ten pieces of data, performing trades based not only on purchases. It continuously asks GPT throughout the day whether to buy, sell, or hold based on new data, following its instructions for trading. My setup involves sending GPT trade data for the chosen stocks every ten minutes from the opening. GPT is then queried, Based on your advice, I've bought this; here's the current trade data for these ten stocks, what should I do now? So, it's about deciding buy, sell, or hold actions among these ten stocks, every ten minutes, following GPT's instructions. This seemed like a fun approach. While I could manually input technical trading algorithms into the code, which would take time but is feasible, the downside of handling this personally, especially for those not trading full-time, is the consistent lack of time and unpredictable work hours.

I felt relying solely on my manual intervention wasn't wise; hence, the idea of letting GPT take on the role, analyze outcomes, and apply continuous trading strategies based on its analysis. It could also report back to me, making the process more convenient. So, I thought, 'Why not?' I've confirmed that theoretically, it's feasible to connect GPT to automated trading, and GPT can indeed make data-driven decisions based on technical analysis. Next steps? Visit the OpenAI website to explore the API services. The website looks great, and here you can see how to connect to their API services. I've already paid for the service; it's not expensive, just link a card. Testing this, you can track your spending, seeing charges for each use. With about $9 left, I reviewed the pricing policies for using the API with different GPT versions, deciding on GPT-3.5 Turbo for cost-efficiency.

Registering my API key allowed me to link my automated trading with GPT, which I'll demonstrate. Secondly, after receiving the API key from OpenAI, I tried connecting it to see if it accurately responds to queries. You can find detailed instructions online for setting up and using the API for queries. Before sending text prompts to GPT, you can assign it a role to specify how it should respond. I gave it a simple role, You are a highly skilled fund manager investing in Korea, applying famous investment strategies... Asking it to recommend stocks based on this setup, I tried running a test with GPT-4. Let's see how it responds. Expecting initial delays, I awaited its advice based on the given investment strategy, querying for stock recommendations. Though I hadn't provided specific data yet, GPT offered a general description of the strategy and asked for data to proceed. This interaction showcases the potential for utilizing GPT in automating trading decisions.

furthering the integration of AI in personal investment strategies. So, utilizing the GPT API for my automated trading program seems entirely feasible. The key is to refine the prompts meticulously, ensuring they precisely match the dataset required for investment decisions. I plan to send queries every ten minutes, asking whether to buy, hold, or sell, and expect responses in the form of a data table. This table will directly influence the trading decisions made by the program. Thus, I've concluded it's possible to integrate GPT effectively. Instead of crafting algorithms myself—acknowledging my limitations in knowledge and time—I'll rely on GPT, which I've already integrated into my trading setup. This setup will continually receive investment decisions based on predefined strategies, significantly automating the process. My role for GPT will be defined as a day trader aiming for a 1% return with an initial capital of 1 million won, allowing it to freely compose a portfolio.

Over time, I can adjust this portfolio, deciding the percentage to allocate to blue-chip stocks, potential high-gain stocks, etc. In addition to market data, I plan to include real-time news updates by connecting a news API, incorporating breaking news into the decision-making process. Though I haven't tested this yet, it appears to be a feasible goal. Another intriguing aspect I discovered while studying the GPT API is its potential for custom application. For example, I've been collecting data on investments and building a database (DB). I realized that it's possible to link this DB to GPT, allowing it to use my data for more precise responses. This capability extends beyond automated trading to practical applications in report writing, offering detailed numerical data and current business conditions. It opens up possibilities for leveraging my data collection in conjunction with GPT for various purposes. In summary, this video discussed my plans to utilize GPT for automating trading strategies.

The potential for GPT to streamline tasks and reduce the need for manual resource allocation in both professional and hobbyist applications is exciting. I plan to connect everything practically and share the outcomes in future videos. If you have any questions, feel free to comment below, and I'll see you in the next video.

GPT4 가 자동매매 프로그램에 연결되면 어떤일이? What Happens When GPT-4 Connects to an Automated Trading Program

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야근하는 회계사

Advancements in Automatic Trading Program

Sangchul Yoon from TacozLabs Accounting is making progress on an automatic trading program using Python and the Korea Investment & Securities API.

To enhance profitability, he is refining the algorithm and considering technical analysis techniques for day trading.

Yoon plans to use the OpenAI GPT API for real-time investment decision-making, aiming to communicate market data and guidance to the trading program.

Using AI for Day Trading: Analyzing KOSPI Stocks

The uploaded CSV file contained three years of price data for five KOSPI-listed companies.

GPT provided technical analysis methods, such as MACD and RSI, to recommend the best stock for immediate investment.

The analysis ranked the tickers based on various criteria, ultimately suggesting ticker 5490 as the most promising option for day trading.

The trading program processes data and queries GPT every ten minutes for buy, sell, or hold decisions on ten stocks.

The approach aims to alleviate the time constraints of manual trading, allowing GPT to analyze outcomes and apply continuous trading strategies.

The author plans to connect to OpenAI's API for GPT services, opting for the cost-effective GPT-3.

Registering for an API key from OpenAI allowed me to link GPT with my automated trading system.

I assigned GPT a role as a fund manager investing in Korea and queried it for stock recommendations based on a specific investment strategy.

The plan is to send queries every ten minutes for buy, hold, or sell decisions, with GPT's responses in a data table to influence the trading program's decisions.

The portfolio will be adjusted over time, with a focus on blue-chip and high-gain stocks, while incorporating real-time news updates through a news API.

The GPT API's potential for custom application is being explored by linking a database of investment data, allowing for more precise responses and practical applications in report writing.

The goal is to streamline tasks and reduce manual resource allocation, with plans to share the outcomes in future videos.