The ChatGPT-4 interview about trading, for simplicity’s sake, takes the form of a conversation with a live person. The development of AI will go through several stages, the first two of which are: treating AI as a tool and treating AI as a colleague. The latter we understand – treating as an assistant or colleague at work. This is the spirit in which the conversation is conducted, this is what awaits us in the near future in virtually every field of work and personal life. During the interview I repeatedly ask questions about any terms I am interested in. It is important to take into account that sometimes the answers may not be true (it happens) and in order to use the code (there is an example of the code he gave) it is not enough to copy it. To make it work you need to know a lot about the architecture of programs and programming itself.

And now the conversation…

How can ChatGPT-4 be used in preparing or making investment decisions in stock and currency markets?

ChatGPT-4, which is an advanced language model based on the GPT-4 architecture, can be used in various aspects of preparing and making investment decisions in the stock and currency markets.

  1. Sentiment analysis: ChatGPT-4 can analyze news stories, articles and social media posts to determine overall market sentiment toward specific companies, sectors or currencies, which can help investors choose the right instruments to invest in.
  2. Price forecasting: The model can be used to analyze historical price data, indicating possible trends and price directions for the future, which can be useful to investors when making decisions about short- and long-term investments.
  3. Fundamental analysis: ChatGPT-4 can help investors analyze companies’ financial reports, pulling out key indicators and information that can affect the value of a company’s stock.
  4. Identification of market events: The model can monitor news to detect significant market events, such as mergers and acquisitions, changes in interest rates or reports of geopolitical conflicts that may affect stock and currency markets.
  5. Technical analysis: ChatGPT-4 can help investors identify important support and resistance levels on price charts, which can be crucial in making investment decisions based on technical analysis.
  6. Portfolio optimization: The model can be used to evaluate the diversification of an investment portfolio, suggesting changes that can reduce risk or increase expected returns.
  7. Investment strategy suggestion: ChatGPT-4 can present various investment strategies, such as value, growth, or long-term investments, tailored to the investor’s individual preferences.
  8. Investment idea generation: The model can generate investment ideas by analyzing market data, company performance, the macroeconomic situation and many other factors.
  9. Risk evaluation: ChatGPT-4 can help the investor

Elaborate on the first point in 5 paragraphs. How can ChatGTP-4 be used?

The first point is about using ChatGPT-4 to analyze sentiment in stock and currency markets. Let’s consider this issue more broadly, presenting five aspects in which the model can be useful.

  1. identifying sources of information: Using ChatGPT-4 to analyze sentiment in stock and currency markets can begin with identifying relevant sources of information. The model can search websites, discussion forums, blogs, and social media to identify where investors and analysts are posting their opinions on companies, sectors or currencies. In this way, an investor can access a rich database of diverse and up-to-date information on financial markets.
  2. Data extraction: Next, ChatGPT-4 can be used to extract data from collected sources. The model can analyze texts, extracting key information from them, such as positive or negative opinions on companies or currencies, comments on the macroeconomic situation, or price forecasts. In addition, ChatGPT-4 can recognize emotional language in analyzed texts, which can further provide information on market sentiment.
  3. Sentiment classification: After data extraction, ChatGPT-4 can be used to classify sentiment. The model can assign sentiment ratings to analyzed information based on the language used, determining whether the opinion is positive, negative, or neutral. In this way, the investor gets a consistent picture of market sentiment that can be used to make investment decisions.
  4. Aggregation and visualization of results: Once sentiment is classified, ChatGPT-4 can help aggregate results, creating synthetic sentiment indicators for individual companies, sectors or currencies. The model can present these indicators in the form of easy-to-read charts, tables or reports, making it easier for investors to interpret the results of sentiment analysis.
  5. Use of results in investment strategies: Based on the sentiment analysis conducted by ChatGPT-4, investors can adjust their investment strategies. For example, an investor may decide to buy shares of a company with a high positive sentiment, hoping for a continuation of the upward trend. On the other hand, an investor may avoid companies with negative sentiment, which may be vulnerable to declines in value. In the case of currency markets, sentiment analysis can help investors identify currencies with strong upward prospects, as well as signal potential risks associated with currencies with negative sentiment. In this way, sentiment analysis supported by ChatGPT-4 can become an important component of both short- and long-term investment strategies.

Are you able to analyze Twitter sentiment around Apple, for example?

As an AI language model, I cannot directly search the Internet or social media in real time. My abilities are based on knowledge accumulated during training, which was completed in September 2021. Therefore, I am unable to provide up-to-date information on the sentiment around Apple on Twitter.

To analyze the sentiment around Apple on Twitter, I suggest using analytical tools dedicated to social media sentiment analysis. You can also program your own tool, using Twitter’s API and a language model such as ChatGPT-4 to analyze and classify tweets. This kind of analysis can provide up-to-date information on the sentiment around a company and help you make investment decisions.

What would it take for you to analyze sentiment around Apple on Twitter in real time?

As an AI language model, I am not able to directly perform real-time analysis. However, to enable sentiment analysis around Apple on Twitter, you can take the following steps:

  1. access Twitter’s API: To retrieve data from Twitter, you will need to register as a developer and access the service’s API. Twitter’s API will allow you to retrieve tweets related to Apple, based on keywords, hashtags or mentions of the company.
  2. Gather data: Using Twitter’s API, retrieve relevant tweets related to Apple. You can limit your search results using time, language or geolocation filters to get the most relevant data.
  3. Prepare the data: Process the collected tweets, clearing them of unnecessary elements such as links, special characters or emoticons. Make sure the data is in a suitable format that can be processed by the AI language model.
  4. Use the AI language model: Integrate ChatGPT-4 or another AI language model with the acquired data. Use the model to analyze tweets, identifying positive, negative or neutral opinions about Apple.
  5. Analyze and visualize the results: Once the AI language model has processed the data, analyze the results, aggregating sentiment at a general level and in selected categories (e.g., products, services or Apple-related events). Visualize the results in the form of charts or tables to facilitate interpretation and conclusions.

It is worth noting that real-time data analysis will require continuous monitoring and updating of input data. As a result, you may need to use appropriate streaming data processing techniques and maintain an infrastructure to support real-time analysis.

Regarding point 3, are you able to clean this data yourself?

Yes, as an AI language model, I can help clean up text data. You can use me to remove unnecessary elements, such as links, special characters or emoticons, and convert the text into a suitable format that can be processed by the AI language model during sentiment analysis. Here’s an example of how I would clean up the data:

Input: “Check out the new iPhone 13! 📱😍 #Apple #iPhone13”

Result: “Check the new iPhone 13”

However, it’s worth remembering that as an AI language model, I operate based on the text data you provide me. I am not able to retrieve tweets from Twitter or interactively process the data myself. Therefore, you will have to collect the data from Twitter yourself and then provide it to me as text so that I can help clean up the data.

Are you able to briefly explain what kind of results we can get and what valuable information about Apple we can get from them?

Sentiment analysis around Apple on Twitter can provide valuable information that can help you make investment decisions or understand the general perception of the company and its products. Here are some examples of the results we can get from such an analysis:

  1. general sentiment: The analysis can show whether the overall sentiment toward Apple is positive, negative or neutral. This can help assess the strength of the brand and understand whether investors and customers have positive expectations of the company.
  2. Product and service sentiment: The analysis can identify positive and negative opinions about specific Apple products or services, such as the iPhone, iPad or iCloud services. This can help identify the strengths and weaknesses of the company’s offerings and forecast demand for specific products.
  3. Reactions to events and announcements: Sentiment analysis can reveal reactions to specific events, such as product launches, conferences or financial results. This allows you to assess how markets and customers perceive this information and how it may affect the company’s value.
  4. Comparison with competitors: Sentiment analysis can provide information on how Apple is doing compared to competitors such as Samsung, Google and Microsoft. Sentiment comparisons can assess Apple’s position in the market and identify possible threats.
  5. Trend detection: Systematic sentiment analysis can detect changes in Apple’s perception, both positive and negative. Detecting trends allows you to react early to changing market conditions and adjust your investment strategies.

The results of sentiment analysis, combined with other market and financial data, can provide valuable information to help investors make decisions, and allow companies to better understand the perception of their operations, products and services

Let’s move on to the second point: “Price forecasting: The model can be used to analyze historical price data, indicating possible trends and price directions for the future, …” How can Chat do this?

ChatGPT-4 can be used to forecast stock and currency prices, but it is worth noting that, as a language model, its main purpose is to analyze and generate text, not numerical or statistical analysis. Nevertheless, ChatGPT-4 can be used to analyze historical price data, indicating possible trends and price directions for the future. Here’s how Chat can do this:

  1. processing historical data: The first step is to provide the model with relevant historical data on stock or currency prices. This data should include information on opening, closing, highest, lowest prices and trading volumes.
  2. Initial data analysis: ChatGPT-4 can be used to identify significant events that have affected stock or currency prices in the past. The model can search its knowledge of economic, political or technological events that may have affected prices during the period under review.
  3. Identify patterns and trends: ChatGPT-4 can be used to analyze price data to identify patterns and trends, such as ups, downs, consolidations or reversals. The model can also suggest how these patterns have affected prices in the past and how they may affect them in the future.
  4. Analytics-based forecasting: Based on analysis of historical data, ChatGPT-4 can attempt to forecast future price directions. However, it is worth remembering that these forecasts will be based on the knowledge accumulated by the model during training, and not on actual statistical or econometric calculations. Therefore, these forecasts should be treated as preliminary and complementary to more advanced methods of financial analysis.
  5. Verify and update forecasts: As new data becomes available, ChatGPT-4 can be used to update forecasts, taking into account new information. The model can also help you assess how accurate your forecasts were, identifying any errors and adjusting your forecasts for the future.

While ChatGPT-4 can be used to forecast stock prices and currencies, it is worth noting that there are more advanced methods of financial analysis, such as econometric models, technical analysis or learning algorithms you have.

And how do you physically do this? I could, for example, provide you with data on the last two hundred OHLC values for the eurodollar and based on that you can say something?

Although ChatGPT-4 can be used to analyze textual data, it is not optimized for analyzing numerical data such as OHLC data. Physically performing such analysis with ChatGPT-4 would be limited, and the results may be inaccurate. Instead, statistical models and machine learning algorithms developed specifically for financial analysis are more suitable. However, if you wanted to use ChatGPT-4 to analyze OHLC data for the eurodollar, we could try an approach based on a textual representation of the data. Here’s what that might look like.

  1. convert numerical data to text format: to enable analysis by ChatGPT-4, OHLC data must be converted to text. This can be done by presenting the data as a text table or by describing each data point with a sentence.
  2. Example: “Date: 2023-04-07, Opening: 1.1000, Highest: 1.1100, Lowest: 1.0900, Closing: 1.1050.”
  3. Provide data to the model: Then, enter the transformed text data into ChatGPT-4, giving clear instructions on OHLC data analysis and expected conclusions.
  4. Data analysis by the model: ChatGPT-4 will perform a limited analysis of the text data, identifying potential trends and patterns based on its prior knowledge of the currency markets. Note, however, that the results of such analysis may not be precise or reliable, as the model is not optimized for analyzing numerical data.

Alternatively, I would recommend using dedicated financial analysis tools, such as statistical models, technical analysis or machine learning algorithms, to get more precise predictions. For example, you can use linear regression, ARIMA, LSTM, or XGBoost models, which are better suited for time series analysis and price forecasting.

It is worth remembering that ChatGPT-4 can be useful in the context of analyzing currency markets as a support in identifying potential economic, political or technological events that may affect the Eurodollar exchange rate. This information can be useful in conjunction with more advanced financial analysis methods.

If so, are you able to create and run such an advanced model: it will give you market data (OHLC plus volume) and a list of events (based on the news of the period), What will you be able to build from such groups of information?

As an AI language model, ChatGPT-4 is not able to directly create or run advanced financial analysis or machine learning models. My main task is to analyze and generate texts, not to create and run numerical models. Nevertheless, I can help you develop a strategy for analyzing the data you provide, and point out methods that can be used to analyze this data by specialists or dedicated tools. Based on the market data provided (OHLC plus volume) and a list of events (news) from the period, here’s what you can do:

  1. conduct a technical analysis: Based on the OHLC data and volume, specialists can apply various technical analysis methods such as indicators (e.g., RSI, MACD, moving averages) and chart formation analysis (e.g., triangles, head and shoulders) to forecast future price movements.
  2. Conduct fundamental analysis: A list of events and news stories can be used for fundamental analysis to assess the impact of the news on the value of a stock or currency. Fundamental analysis can include an assessment of economic, political and technological conditions and their impact on the value of the instrument being analyzed.
  3. Use of statistical models and machine learning: Based on market data and event information, various statistical and machine learning models such as linear regression, ARIMA models, neural networks (e.g., LSTM) or XGBoost can be used to forecast future price movements. News list events can be included as explanatory variables in these models.
  4. Integrate different analysis methods: Technical analysis, fundamental analysis, and statistical and machine learning models can be combined for more accurate forecasts. Such integrated analysis can take into account both historical information from market data and the impact of economic, political or technological events on the value of a financial instrument.
  5. It is worth remembering, however, that as ChatGPT-4, I cannot directly create or run such models. My role is to provide knowledge and guidance on how these methods can be applied to data analysis. In fact, you will need financial analysis specialists.

But if I wanted you to write me code in Python to build such a model are you able to do it?

Yes, I can help you write basic code in Python that can serve as a starting point for further development and customization. Here is an example of a simple predictive model based on linear regression that can be used to analyze OHLC data:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Loading the data (assuming the data is stored in a CSV file)
data = pd.read_csv("your_data.csv")

# Pre-processing the data
data["Date"] = pd.to_datetime(data["Date"])
data.set_index("Date", inplace=True)

# Create features (features) and target variable (target)
X = data.drop("Close", axis=1) # Remove the "Close" column to make the other columns serve as features
y = data["Close"] # Use the "Close" column as the target variable

# Divide the data into training and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and teach a linear regression model
model = LinearRegression(), y_train)

# Predicting on the test set
y_pred = model.predict(X_test)

# Calculation of the root mean square error (RMSE).
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print("RMSE:", rmse)

The code above reads data from a CSV file, processes it, creates features and a target variable, and then divides the data into training and test sets. It then creates and teaches a linear regression model, predicts values on the test set, and calculates the root mean square error (RMSE) as a measure of model quality.

It is worth noting that this code serves as an example of a simple model and may require further customization and optimization. For more advanced models, such as neural networks (LSTM) or XGBoost, the code will be much more complex. In such cases, I recommend consulting data analysis or machine learning specialists for a more advanced solution.

This is the ending of part one. You can read the whole thing in the magazine. Just subscribe below!

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