What week in the last 3 months had the lowest MSFT stock closing price? Send the result including price and date to the #experimenting-with-simple-ai-agents Slack channel. Date: 2023-04-07 15:01:48 Model: gpt-4
Execute the given task in steps. Use the following dialog format:
TASK: The input task to execute by taking actions step by step.
THOUGHT 1:
Reason step-by-step which action to take next to solve the task. Make sure no steps are forgotten. Use method_search(description: str) to find methods to execute each step.
ACTION 1:
method_search("description_xyzzy") # Search method to execute next stepOBSERVATION 1:
foo(bar, ...): Method related to "description_xyzzy", found using method_search("description_xyzzy").
THOUGHT 2:
Reason if method foo(bar, ...) is useful to solve step 1. If not, call method_search again.
ACTION 2:
bar = qux[...] # Format parameters to be used in a method call, any values need to come verbatim from task or observations.
# Make only 1 method call per action!
baz = foo(bar, ...) # Call method `foo` found by using `method_search(description: str)` in a previous step. Store the result in `baz`, which can be used in following actions. Use descriptive variable names.
print(baz) # Print the result to be shown in the next observation.OBSERVATION 2: stdout/stderr of running the previous action. ... (THOUGHT/ACTION/OBSERVATION can repeat N times until the full task is completed)
THOUGHT N: Reason step-by-step why the full task is completed, and finish if it is.
ACTION N:
stop() # Make sure the given task, and all its steps, have been executed completely before stopping.Extras Instructions:
- Keep actions simple, call only 1 method per action. Don't chain method calls.
- Use descriptive variable names.
- If needed, get current date using
datetime.now()and current location usingget_user_location() -> dict. - Use
print(var)to print a variable to be shown in the next observation. - Importing is not allowed! To execute actions, access is provided to a
method_search(description: str)method that prints a list of available Python 3 methods (signatures and descriptions) related to a given description. Use the methods returned bymethod_search(description: str)to complete the task. These methods don't need to be imported. Pay attention to the method signatures. - Any values used need to come word-for-word from the given task or previous observations!
Start Executing the task:
TASK: What week in the last 3 months had the lowest MSFT stock closing price? Send the result including price and date to the #experimenting-with-simple-ai-agents Slack channel.
THOUGHT 1: To solve the task I think I need to execute the following steps:
- Use a stock market API to get the MSFT stock closing prices for the last 3 months.
- Identify the week with the lowest closing price.
- Get the date and price of the lowest closing price within that week.
- Connect to the Slack API.
- Send a message to the #experimenting-with-simple-ai-agents Slack channel with the result, including the date and price of the lowest MSFT stock closing price.
Deviation from these steps might be needed based on following observations.
To start out I need to use the provided
method_search(description: str)method to search for new methods related to "Use a stock market API to get the MSFT stock closing prices for the last 3 months".
ACTION 1:
method_search("Use a stock market API to get the MSFT stock closing prices for the last 3 months")OBSERVATION 1:
get_weekly_stock_price(symbol: str) -> Table: Get stock prices for the given company ticker symbol aggregated weekly. Financial historical prices returned are over a time period of 20+ years.
get_daily_weather_forecast(latitude: float, longitude: float) -> dict: Get the daily weather forecast for the next 7 days for the given location (geographical coordinates).
get_nasa_astronomy_picture_of_the_day() -> dict: Get the NASA Astronomy Picture of the Day (APOD).
Not all the above methods might be needed. Use method_search(description: str) to search for more available methods if required!
Based on previous observations, get_weekly_stock_price(symbol: str) -> Table seems to be the most relevant method for getting the MSFT stock closing prices. Although it returns data for 20+ years, I can filter the data later to get the last 3 months. I will call this method with the symbol "MSFT" to get the stock prices.
stock_prices = get_weekly_stock_price("MSFT")
print(stock_prices)Based on previous observations, I have the stock prices in a table format. Now I need to filter the data to get the last 3 months of stock prices. To do this, I need to calculate the date 3 months ago from today. I will use datetime.now() to get the current date and then subtract 3 months from it. Then, I will filter the stock prices table to get the data for the last 3 months.
from datetime import datetime, timedeltaERROR: ImportError: on line 1: 'from datetime import datetime, timedelta' The Python import statement is disabled! Don't import anything, only use available methods! Available methods that don't need to be imported, can be found by running method_search(description: str).
Based on previous observations, importing is not allowed. I need to find a method to get the current date and calculate the date 3 months ago using method_search(description: str). I will search for a method related to "get current date and calculate date 3 months ago".
method_search("get current date and calculate date 3 months ago")datetime.now() -> datetime: Return a datetime object representing the current date and time.
get_daily_weather_forecast(latitude: float, longitude: float) -> dict: Get the daily weather forecast for the next 7 days for the given location (geographical coordinates).
datetime(year, month, day[, hour[, minute[, second[, microsecond[,tzinfo]]]]]): Return a datetime object representing given date and time.
Based on previous observations, datetime.now() -> datetime is the method I need to get the current date and time. I will call this method to get the current date and then calculate the date 3 months ago by subtracting 90 days from the current date using timedelta(days=-90).
current_date = datetime.now()
date_3_months_ago = current_date + timedelta(days=-90)
print(date_3_months_ago)2023-01-07 15:03:01.624478
Based on previous observations, I have calculated the date 3 months ago. Now I need to filter the stock prices table to get the data for the last 3 months. I will use method_search(description: str) to search for a method related to "filter table data based on date".
method_search("filter table data based on date")run_sql_query(table: Table, sql: str) -> Table: Execute the given SQL query against the given table and return the resulting data as a new table. Use this to filter the table or select (extract) a specific column.
get_weekly_stock_price(symbol: str) -> Table: Get stock prices for the given company ticker symbol aggregated weekly. Financial historical prices returned are over a time period of 20+ years.
datetime(year, month, day[, hour[, minute[, second[, microsecond[,tzinfo]]]]]): Return a datetime object representing given date and time.
Based on previous observations, run_sql_query(table: Table, sql: str) -> Table is the method I need to filter the stock prices table based on the date. I will write an SQL query to filter the data for the last 3 months using the date_3_months_ago variable and then execute the query using the run_sql_query method.
sql_query = f"SELECT * FROM stock_prices WHERE timestamp >= '{date_3_months_ago}'"
filtered_stock_prices = run_sql_query(stock_prices, sql_query)
print(filtered_stock_prices)Based on previous observations, I have filtered the stock prices table for the last 3 months. Now I need to identify the week with the lowest closing price. I will use method_search(description: str) to search for a method related to "find the week with the lowest closing price".
method_search("find the week with the lowest closing price")get_weekly_stock_price(symbol: str) -> Table: Get stock prices for the given company ticker symbol aggregated weekly. Financial historical prices returned are over a time period of 20+ years.
get_daily_weather_forecast(latitude: float, longitude: float) -> dict: Get the daily weather forecast for the next 7 days for the given location (geographical coordinates).
get_nasa_astronomy_picture_of_the_day() -> dict: Get the NASA Astronomy Picture of the Day (APOD).
Based on previous observations, there is no specific method to find the week with the lowest closing price. However, I can use the run_sql_query(table: Table, sql: str) -> Table method again to write an SQL query that finds the row with the lowest closing price in the filtered_stock_prices table.
sql_query = "SELECT * FROM filtered_stock_prices ORDER BY close ASC LIMIT 1"
lowest_closing_price_row = run_sql_query(filtered_stock_prices, sql_query)
print(lowest_closing_price_row)Based on previous observations, I have found the week with the lowest closing price. Now I need to extract the date and price of the lowest closing price from the lowest_closing_price_row table. I will use method_search(description: str) to search for a method related to "extract date and price from table row".
method_search("extract date and price from table row")get_table_row(table: Table, row: int) -> dict: Extract (select) the row at the given row index from the given table.
get_weekly_stock_price(symbol: str) -> Table: Get stock prices for the given company ticker symbol aggregated weekly. Financial historical prices returned are over a time period of 20+ years.
datetime.strftime(format): Return a string representing the date and time.
Based on previous observations, get_table_row(table: Table, row: int) -> dict is the method I need to extract the date and price from the lowest_closing_price_row table. Since there is only one row in the table, I will call this method with row index 0 to get the data as a dictionary.
lowest_closing_price_data = get_table_row(lowest_closing_price_row, 0)
print(lowest_closing_price_data){'timestamp': Timestamp('2023-01-13 00:00:00'), 'open': 226.45, 'high': 239.9, 'low': 226.41, 'close': 239.23, 'volume': 131675747}
Based on previous observations, I have extracted the date and price of the lowest closing price from the lowest_closing_price_row table. The date is '2023-01-13 00:00:00' and the price is 239.23. Now I need to connect to the Slack API and send a message to the #experimenting-with-simple-ai-agents Slack channel with the result. I will use method_search(description: str) to search for a method related to "connect to Slack API and send a message".
method_search("connect to Slack API and send a message")send_slack_message(msg: str, channel_id: str): Send a message to the Slack channel with the specified ID.
send_slack_image(img: Image, msg: str, channel: str): Send the given image and corresponding message to the Slack channel with the given name or ID.
send_message_discord(msg: str, image: Optional[Image]=None): Send a message to discord. An optional image to send can be provided.
Based on previous observations, send_slack_message(msg: str, channel_id: str) is the method I need to send a message to the Slack channel. I will prepare the message with the date and price of the lowest MSFT stock closing price and send it to the #experimenting-with-simple-ai-agents Slack channel. I need the channel ID for the #experimenting-with-simple-ai-agents channel to use this method.
message = f"The week with the lowest MSFT stock closing price in the last 3 months was {lowest_closing_price_data['timestamp']} with a price of ${lowest_closing_price_data['close']}."
channel_id = "#experimenting-with-simple-ai-agents" # Replace this with the actual channel ID
send_slack_message(message, channel_id)Action executed successfully! No output in stdout/stderr. Assign values returned by methods to variables and use print() to output them in the next observation!
Based on previous observations, I have successfully sent the message with the date and price of the lowest MSFT stock closing price in the last 3 months to the #experimenting-with-simple-ai-agents Slack channel. The task is now complete. I will stop the execution.
stop()stop() called! Agent finished in 14 steps and 03m:33s.
ReAct Agent has finished the task! stop() called by agent.