Finance Monitoring Agent
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Finance Monitoring Agent
jakub.kopecky/finance-monitoring-agent
Try for free
No credit card required
The Finance Monitoring AI Agent 📊💹 analyzes specific tickers, gathering and processing data to generate insightful reports 📈📉. Designed for investors and analysts, this agent provides detailed performance analysis and trends. 🚀
.dockerignore
1# apify
2storage/
3
4# Byte-compiled / optimized / DLL files
5__pycache__/
6*.py[cod]
7*$py.class
8
9# C extensions
10*.so
11
12# Distribution / packaging
13.Python
14build/
15develop-eggs/
16dist/
17downloads/
18eggs/
19.eggs/
20lib/
21lib64/
22parts/
23sdist/
24var/
25wheels/
26share/python-wheels/
27*.egg-info/
28.installed.cfg
29*.egg
30MANIFEST
31
32# PyInstaller
33# Usually these files are written by a python script from a template
34# before PyInstaller builds the exe, so as to inject date/other infos into it.
35*.manifest
36*.spec
37
38# Installer logs
39pip-log.txt
40pip-delete-this-directory.txt
41
42# Unit test / coverage reports
43htmlcov/
44.tox/
45.nox/
46.coverage
47.coverage.*
48.cache
49nosetests.xml
50coverage.xml
51*.cover
52*.py,cover
53.hypothesis/
54.pytest_cache/
55cover/
56
57# Translations
58*.mo
59*.pot
60
61# Django stuff:
62*.log
63local_settings.py
64db.sqlite3
65db.sqlite3-journal
66
67# Flask stuff:
68instance/
69.webassets-cache
70
71# Scrapy stuff:
72.scrapy
73
74# Sphinx documentation
75docs/_build/
76
77# PyBuilder
78.pybuilder/
79target/
80
81# Jupyter Notebook
82.ipynb_checkpoints
83
84# IPython
85profile_default/
86ipython_config.py
87
88# pyenv
89# For a library or package, you might want to ignore these files since the code is
90# intended to run in multiple environments; otherwise, check them in:
91# .python-version
92
93# pipenv
94# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
95# However, in case of collaboration, if having platform-specific dependencies or dependencies
96# having no cross-platform support, pipenv may install dependencies that don't work, or not
97# install all needed dependencies.
98#Pipfile.lock
99
100# UV
101# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
102# This is especially recommended for binary packages to ensure reproducibility, and is more
103# commonly ignored for libraries.
104#uv.lock
105
106# poetry
107# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
108# This is especially recommended for binary packages to ensure reproducibility, and is more
109# commonly ignored for libraries.
110# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
111#poetry.lock
112
113# pdm
114# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
115#pdm.lock
116# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
117# in version control.
118# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
119.pdm.toml
120.pdm-python
121.pdm-build/
122
123# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
124__pypackages__/
125
126# Celery stuff
127celerybeat-schedule
128celerybeat.pid
129
130# SageMath parsed files
131*.sage.py
132
133# Environments
134.env
135.venv
136env/
137venv/
138ENV/
139env.bak/
140venv.bak/
141
142# Spyder project settings
143.spyderproject
144.spyproject
145
146# Rope project settings
147.ropeproject
148
149# mkdocs documentation
150/site
151
152# mypy
153.mypy_cache/
154.dmypy.json
155dmypy.json
156
157# Pyre type checker
158.pyre/
159
160# pytype static type analyzer
161.pytype/
162
163# Cython debug symbols
164cython_debug/
165
166# PyCharm
167# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
168# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
169# and can be added to the global gitignore or merged into this file. For a more nuclear
170# option (not recommended) you can uncomment the following to ignore the entire idea folder.
171#.idea/
172
173# Ruff stuff:
174.ruff_cache/
175
176# PyPI configuration file
177.pypirc.gitignore
1# apify
2storage/
3
4# Byte-compiled / optimized / DLL files
5__pycache__/
6*.py[cod]
7*$py.class
8
9# C extensions
10*.so
11
12# Distribution / packaging
13.Python
14build/
15develop-eggs/
16dist/
17downloads/
18eggs/
19.eggs/
20lib/
21lib64/
22parts/
23sdist/
24var/
25wheels/
26share/python-wheels/
27*.egg-info/
28.installed.cfg
29*.egg
30MANIFEST
31
32# PyInstaller
33# Usually these files are written by a python script from a template
34# before PyInstaller builds the exe, so as to inject date/other infos into it.
35*.manifest
36*.spec
37
38# Installer logs
39pip-log.txt
40pip-delete-this-directory.txt
41
42# Unit test / coverage reports
43htmlcov/
44.tox/
45.nox/
46.coverage
47.coverage.*
48.cache
49nosetests.xml
50coverage.xml
51*.cover
52*.py,cover
53.hypothesis/
54.pytest_cache/
55cover/
56
57# Translations
58*.mo
59*.pot
60
61# Django stuff:
62*.log
63local_settings.py
64db.sqlite3
65db.sqlite3-journal
66
67# Flask stuff:
68instance/
69.webassets-cache
70
71# Scrapy stuff:
72.scrapy
73
74# Sphinx documentation
75docs/_build/
76
77# PyBuilder
78.pybuilder/
79target/
80
81# Jupyter Notebook
82.ipynb_checkpoints
83
84# IPython
85profile_default/
86ipython_config.py
87
88# pyenv
89# For a library or package, you might want to ignore these files since the code is
90# intended to run in multiple environments; otherwise, check them in:
91# .python-version
92
93# pipenv
94# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
95# However, in case of collaboration, if having platform-specific dependencies or dependencies
96# having no cross-platform support, pipenv may install dependencies that don't work, or not
97# install all needed dependencies.
98#Pipfile.lock
99
100# UV
101# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
102# This is especially recommended for binary packages to ensure reproducibility, and is more
103# commonly ignored for libraries.
104#uv.lock
105
106# poetry
107# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
108# This is especially recommended for binary packages to ensure reproducibility, and is more
109# commonly ignored for libraries.
110# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
111#poetry.lock
112
113# pdm
114# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
115#pdm.lock
116# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
117# in version control.
118# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
119.pdm.toml
120.pdm-python
121.pdm-build/
122
123# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
124__pypackages__/
125
126# Celery stuff
127celerybeat-schedule
128celerybeat.pid
129
130# SageMath parsed files
131*.sage.py
132
133# Environments
134.env
135.venv
136env/
137venv/
138ENV/
139env.bak/
140venv.bak/
141
142# Spyder project settings
143.spyderproject
144.spyproject
145
146# Rope project settings
147.ropeproject
148
149# mkdocs documentation
150/site
151
152# mypy
153.mypy_cache/
154.dmypy.json
155dmypy.json
156
157# Pyre type checker
158.pyre/
159
160# pytype static type analyzer
161.pytype/
162
163# Cython debug symbols
164cython_debug/
165
166# PyCharm
167# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
168# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
169# and can be added to the global gitignore or merged into this file. For a more nuclear
170# option (not recommended) you can uncomment the following to ignore the entire idea folder.
171#.idea/
172
173# Ruff stuff:
174.ruff_cache/
175
176# PyPI configuration file
177.pypirc.python-version
13.13Makefile
1DIRS_WITH_CODE = src/ tests/
2
3.PHONY: clean
4clean:
5 rm -rf .mypy_cache .pytest_cache .ruff_cache build dist htmlcov .coverage
6
7.PHONY: install-dev
8install-dev:
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10
11.PHONY: lint
12lint:
13 uv run ruff format --check $(DIRS_WITH_CODE)
14 uv run ruff check --preview $(DIRS_WITH_CODE)
15
16.PHONY: type-check
17type-check:
18 uv run mypy $(DIRS_WITH_CODE)
19
20.PHONY: format
21format:
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23 uv run ruff format $(DIRS_WITH_CODE)
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29.PHONY: check
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1[project]
2name = "finance-monitoring-agent"
3version = "0.1.0"
4description = "Apify Actor AI Agent for monitoring finance tickers in LangGraph."
5readme = "README.md"
6requires-python = ">=3.13"
7dependencies = [
8 "apify>=2.2.1",
9 "langchain-apify>=0.1.1",
10 "langchain-openai>=0.3.4",
11 "langgraph>=0.2.70",
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13
14[tool.ruff]
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23 "COM812", # Missing trailing comma
24 "ISC001", # Single line implicit concatenation
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44 "SLF001", # Private member accessed: `{name}`
45 "T20", # flake8-print
46 "TRY301", # Abstract `raise` to an inner function
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48"**/{docs}/**" = [
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50 "INP001", # File {filename} is part of an implicit namespace package, add an __init__.py
51 "F841", # Local variable {variable} is assigned to but never used
52 "N999", # Invalid module name
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55[tool.ruff.lint.flake8-quotes]
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59[tool.ruff.lint.flake8-type-checking]
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65[tool.ruff.lint.flake8-builtins]
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68[tool.ruff.lint.pydocstyle]
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71[tool.ruff.lint.pylint]
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84disallow_untyped_calls = true
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1441].actor/Dockerfile
1FROM apify/actor-python:3.13
2
3WORKDIR /usr/src/app
4
5COPY pyproject.toml uv.lock ./
6
7ARG ACTOR_PATH_IN_DOCKER_CONTEXT
8ENV ACTOR_PATH_IN_DOCKER_CONTEXT="${ACTOR_PATH_IN_DOCKER_CONTEXT}"
9
10RUN echo "Python version:" \
11 && python --version \
12 && echo "Pip version:" \
13 && pip --version \
14 && echo "Installing uv:" \
15 && pip install --no-cache-dir uv \
16 && echo "Installing dependencies:" \
17 && uv sync --frozen --no-dev \
18 && echo "All installed Python packages:" \
19 && uv pip freeze
20
21COPY src ./src
22
23# create non-root user
24RUN useradd -m apify && \
25 chown -R apify:apify /usr/src/app
26
27USER apify
28
29CMD ["uv", "run", "--no-dev", "--frozen", "python3", "-m", "src"].actor/actor.json
1{
2 "actorSpecification": 1,
3 "name": "finance-monitoring-agent",
4 "title": "Use AI agent to monitor stock market and generate report",
5 "description": "Use AI agent to monitor stock market and generate report",
6 "version": "0.0",
7 "buildTag": "latest",
8 "input": "./input_schema.json",
9 "storages": {
10 "dataset": "./dataset_schema.json"
11 },
12 "dockerfile": "./Dockerfile"
13}.actor/dataset_schema.json
1{
2 "actorSpecification": 1,
3 "views": {
4 "overview": {
5 "title": "Overview",
6 "transformation": {
7 "fields": ["ticker", "sentiment", "sentiment_reason", "report"]
8 },
9 "display": {
10 "component": "table",
11 "properties": {
12 "ticker": {
13 "label": "Ticker",
14 "format": "text"
15 },
16 "sentiment": {
17 "label": "Sentiment",
18 "format": "text"
19 },
20 "sentiment_reason": {
21 "label": "Sentiment Reason",
22 "format": "text"
23 },
24 "report": {
25 "label": "Report",
26 "format": "text"
27 }
28 }
29 }
30 }
31 }
32}.actor/input_schema.json
1{
2 "title": "Finance Monitoring Agent",
3 "type": "object",
4 "schemaVersion": 1,
5 "properties": {
6 "ticker": {
7 "title": "Ticker",
8 "type": "string",
9 "description": "The ticker of the stock to analyze (e.g. TSLA, AAPL, AMZN).",
10 "editor": "textfield",
11 "prefill": "TSLA"
12 },
13 "model": {
14 "title": "OpenAI model",
15 "type": "string",
16 "description": "The OpenAI model to use. Currently supported models are gpt-4o and gpt-4o-mini and the reasoning models o1 and o3-mini. For best results, use gpt-4o.",
17 "enum": [
18 "gpt-4o",
19 "gpt-4o-mini",
20 "o1",
21 "o3-mini"
22 ],
23 "default": "gpt-4o",
24 "prefill": "gpt-4o-mini"
25 },
26 "openai_api_key": {
27 "title": "OpenAI API Key",
28 "type": "string",
29 "description": "The API key for the OpenAI API.",
30 "editor": "textfield",
31 "isSecret": true
32 },
33 "debug": {
34 "title": "Debug",
35 "type": "boolean",
36 "description": "If enabled, the Actor will run in debug mode and produce more output.",
37 "editor": "checkbox",
38 "default": false
39 }
40 },
41 "required": ["ticker", "model", "openai_api_key"]
42}src/__init__.py
src/__main__.py
1import asyncio
2
3from .main import main
4
5# Execute the Actor entry point.
6asyncio.run(main())src/agents.py
1"""This module contains the agents/nodes for the finance monitoring agent graph.
2
3They are the building blocks of the graph and are used to perform specific tasks. For example,
4in this case, the supervisor node controls the flow of the agents and determines the next appropriate action.
5The other two agents are used to analyze the stock ticker and create a report based on the analysis for the user.
6"""
7
8import datetime
9import logging
10from typing import Literal
11
12from langchain_core.messages import ToolMessage
13from langchain_core.runnables.config import RunnableConfig
14from langgraph.prebuilt import create_react_agent
15from langgraph.types import Command
16
17from src.llm import ChatOpenAISingleton
18from src.models import OutputTickerReport
19from src.state import State
20from src.tools import (
21 tool_get_google_news,
22 tool_get_ticker_basic_info,
23 tool_get_ticker_price_targets,
24 tool_get_ticker_recommendations,
25 tool_get_yahoo_ticker_news,
26)
27
28logger = logging.getLogger('apify')
29
30
31async def agent_analysis(state: State, config: RunnableConfig) -> dict:
32 """Agent to analyze the stock ticker.
33
34 This agent uses tools to gather relevant data about the stock ticker and summarize it.
35 The summary is then passed to the next agent.
36
37 Returns:
38 dict: graph state update with the analysis.
39 """
40 llm = ChatOpenAISingleton.get_instance()
41 tools = [
42 tool_get_ticker_basic_info,
43 tool_get_yahoo_ticker_news,
44 tool_get_ticker_price_targets,
45 tool_get_ticker_recommendations,
46 tool_get_google_news,
47 ]
48 subgraph = create_react_agent(llm, tools)
49
50 messages = [
51 (
52 'user',
53 (
54 'You are an AI agent specialized in finance data gathering and summarization. '
55 'Your job is to use tools to gather relevant data about the stock ticker '
56 'and summarize it. Be sure to include news and important information, '
57 'analyst recommendations, and price targets. '
58 'Gather information from as many sources as you have access to. '
59 'For example if you have tools available for news from Yahoo, Google, and X.com, use all of them. '
60 'If you have a source URL link available, for example for news, you must include it in the summary. '
61 'If for some reason any tool fails, try to rerun it once more.'
62 '\n'
63 f'Ticker: {state["ticker"]}\n'
64 f"Today's date: {datetime.datetime.now(tz=datetime.UTC).strftime('%Y-%m-%d')}"
65 ),
66 )
67 ]
68
69 debug = config.get('configurable', {}).get('debug', False)
70 if debug:
71 response: dict = {}
72 async for substate in subgraph.astream({'messages': messages}, stream_mode='values'):
73 message = substate['messages'][-1]
74 # traverse all tool messages and print them
75 if isinstance(message, ToolMessage):
76 # until the analyst message with tool_calls
77 for _message in substate['messages'][::-1]:
78 if hasattr(_message, 'tool_calls'):
79 break
80 logger.debug('-------- Tool --------')
81 logger.debug('Message: %s', _message)
82 continue
83
84 logger.debug('-------- Analyst --------')
85 logger.debug('Message: %s', message)
86 response = substate
87 else:
88 response = await subgraph.ainvoke({'messages': messages})
89
90 return {'analysis': response['messages'][-1].content}
91
92
93def agent_report(state: State) -> dict:
94 """Agent to create a report based on the analysis.
95
96 This agent generates a report about the stock ticker using the ticker data summary.
97
98 Returns:
99 dict: graph state update with the report.
100
101 Raises:
102 ValueError: If analysis is missing.
103 """
104 llm = ChatOpenAISingleton.get_instance()
105 llm_structured = llm.with_structured_output(OutputTickerReport)
106
107 if not state.get('analysis'):
108 msg = 'Analysis is missing!'
109 raise ValueError(msg)
110
111 messages = [
112 (
113 'user',
114 (
115 'You are an AI agent for finance report generation. '
116 'Create a comprehensive report about the stock ticker using the provided data. '
117 'If you have a source URL link available, for example, for news, you must include it in the report. '
118 'DO NOT MAKE UP ANY DATA. IF YOU DO NOT KNOW SOMETHING, LEAVE IT OUT. '
119 'DO NOT TRY TO INTERACT WITH THE USER, ONLY CREATE A REPORT. '
120 'THE REPORT OUTLINE MUST BE AS FOLLOWS (MD format):\n'
121 "- Executive summary - a brief overview of the news from the analysis and the company's "
122 'financial health.\n'
123 '- News - recent news and events that may have affected the stock price.\n'
124 '- Stock price - current stock price and price targets.\n'
125 '- Analyst recommendations - current analyst recommendations for the stock.\n'
126 '- Conclusion - final thoughts on the stock and its future prospects.\n'
127 f'Ticker: {state["ticker"]}\n'
128 f"Today's date: {datetime.datetime.now(tz=datetime.UTC).strftime('%Y-%m-%d')}"
129 ),
130 ),
131 ('user', f'Here is the ticker news and analysis:\n{state["analysis"]}'),
132 ]
133 return {'report': llm_structured.invoke(messages)}
134
135
136# this can be an agent if the graph gets more complex
137def supervisor(state: State) -> Command[Literal['agent_analysis', 'agent_report']]:
138 """Supervisor node to control the flow of the agents.
139
140 This node supervises the agents and determines the next appropriate action based on the current state and
141 updates the state accordingly for the user.
142
143 Returns:
144 Command: Command with status update and go to next agent.
145 """
146 analysis_done = bool(state.get('analysis'))
147
148 if not analysis_done:
149 status = 'gathering and analyzing data...'
150 next_agent = 'agent_analysis'
151 else:
152 status = 'creating report...'
153 next_agent = 'agent_report'
154
155 return Command(goto=next_agent, update={'status': status})src/graph.py
1"""Graph definition module.
2
3This module contains the graph definition for the agent.
4Resources:
5 - https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs
6"""
7
8from langgraph.checkpoint.memory import MemorySaver
9from langgraph.graph import END, StateGraph
10from langgraph.graph.state import CompiledStateGraph
11
12from src.agents import agent_analysis, agent_report, supervisor
13from src.state import State
14
15
16def build_compiled_graph() -> CompiledStateGraph:
17 """Build the compiled state graph for the agent.
18
19 Returns:
20 CompiledStateGraph: Compiled graph.
21 """
22 builder = StateGraph(State)
23 builder.add_node(supervisor)
24 builder.add_node(agent_analysis)
25 builder.add_node(agent_report)
26
27 builder.set_entry_point('supervisor')
28 builder.add_edge('agent_analysis', 'supervisor')
29 builder.add_edge('agent_report', END)
30
31 memory = MemorySaver()
32 return builder.compile(checkpointer=memory)src/llm.py
1"""This module contains the singleton class for ChatOpenAI instance."""
2
3from langchain_openai import ChatOpenAI
4
5
6class ChatOpenAISingleton:
7 """Singleton class for ChatOpenAI instance."""
8
9 _instance: ChatOpenAI | None = None
10
11 @classmethod
12 def create_get_instance(cls, model: str) -> ChatOpenAI:
13 """Creates and returns ChatOpenAI instance, used for creating the singleton instance.
14
15 Returns:
16 ChatOpenAI: ChatOpenAI instance
17 """
18 if cls._instance is None:
19 cls._instance = ChatOpenAI(model=model)
20 return cls._instance
21
22 @classmethod
23 def get_instance(cls) -> ChatOpenAI:
24 """Gets ChatOpenAI instance.
25
26 Returns:
27 ChatOpenAI: ChatOpenAI instance.
28
29 Raises:
30 ValueError: If instance is not created yet.
31 """
32 if cls._instance is None:
33 msg = 'ChatOpenAI instance not created yet!'
34 raise ValueError(msg)
35 return cls._instancesrc/main.py
1import logging
2import os
3from typing import TYPE_CHECKING
4
5from apify import Actor
6
7from src.graph import build_compiled_graph
8from src.llm import ChatOpenAISingleton
9
10if TYPE_CHECKING:
11 from langchain_core.runnables.config import RunnableConfig
12
13 from src.models import OutputTickerReport
14
15logger = logging.getLogger('apify')
16
17
18async def main() -> None:
19 """Actor entry point.
20
21 Raises:
22 ValueError: If required input attributes are missing
23 """
24 async with Actor:
25 # Handle input
26 actor_input = await Actor.get_input()
27 ticker = actor_input.get('ticker')
28 openai_api_key = actor_input.get('openai_api_key')
29 model = actor_input.get('model')
30 debug = actor_input.get('debug', False)
31 if debug:
32 logger.setLevel(logging.DEBUG)
33 if not ticker:
34 msg = 'Missing "ticker" attribute in input!'
35 raise ValueError(msg)
36 if not openai_api_key:
37 msg = 'Missing "openai_api_key" attribute in input!'
38 raise ValueError(msg)
39 os.environ['OPENAI_API_KEY'] = openai_api_key
40 # Create ChatOpenAI singleton instance
41 ChatOpenAISingleton.create_get_instance(model=model)
42
43 # Create the graph
44 config: RunnableConfig = {'configurable': {'thread_id': '1', 'debug': debug}}
45 graph = build_compiled_graph()
46 graph.update_state(config, {'ticker': ticker})
47
48 # Run the graph
49 inputs: dict = {'messages': []}
50 report: OutputTickerReport | None = None
51 actor_status = None
52 async for state in graph.astream(inputs, config, stream_mode='values'):
53 logger.debug('-------- State --------')
54 logger.debug('State: %s', state)
55 status = state.get('status')
56 if status and status != actor_status:
57 await Actor.set_status_message(f'Agent: {status}')
58 logger.info('Agent: %s', status)
59 actor_status = status
60
61 if report := state.get('report'):
62 break
63
64 if not report:
65 msg = 'Failed to generate the report!'
66 raise ValueError(msg)
67
68 logger.info('-------- Report --------')
69 logger.info('Report: %s', report)
70
71 # Add report disclaimer
72 output_report = (
73 f'{report.report}'
74 '\n\n\n---\n\n\nThis report is generated by an AI agent and should not be considered as financial advice.\n'
75 )
76
77 store = await Actor.open_key_value_store()
78 await store.set_value('report.md', output_report)
79 logger.info('Saved the "report.md" file into the key-value store!')
80
81 await Actor.push_data(
82 {
83 'ticker': report.ticker,
84 'sentiment': report.sentiment,
85 'sentiment_reason': report.sentiment_reason,
86 'report': output_report,
87 }
88 )
89 logger.info('Pushed the report to the dataset!')src/models.py
1from pydantic import BaseModel, Field
2
3
4class TickerPriceTarget(BaseModel):
5 """Analyst price targets for a ticker."""
6
7 current_price: float
8 analyst_price_target_low: float
9 analyst_price_target_high: float
10 analyst_price_target_mean: float
11 analyst_price_target_median: float
12
13
14class TickerInfo(BaseModel):
15 """Information about a ticker."""
16
17 sector: str
18 industry: str
19 description: str
20
21
22class TickerNewsEntry(BaseModel):
23 """News entry about a ticker."""
24
25 title: str
26 provider: str = Field(..., description='Provider of the news')
27 published_at: str
28 summary: str | None = None
29 url: str
30
31
32class TickerRecommendationEntry(BaseModel):
33 """Recommendation entry for a ticker."""
34
35 period: str = Field(..., description='Period')
36 recommendations_strong_buy: int = Field(..., description='Number of strong buy recommendations')
37 recommendations_buy: int = Field(..., description='Number of buy recommendations')
38 recommendations_hold: int = Field(..., description='Number of hold recommendations')
39 recommendations_sell: int = Field(..., description='Number of sell recommendations')
40 recommendations_strong_sell: int = Field(..., description='Number of strong sell recommendations')
41
42
43class SupervisorOutput(BaseModel):
44 """Structured output from the supervisor agent."""
45
46 next_agent: str = Field(..., description='Next agent to run')
47 status: str = Field(
48 ...,
49 description=(
50 'Current status of analysis, is one of the following:\n'
51 'gathering and analyzing data...\n'
52 'creating report...\n'
53 ),
54 )
55
56
57class OutputTickerReport(BaseModel):
58 """Structured output report for a ticker from the report agent."""
59
60 ticker: str = Field(..., description='Ticker symbol')
61
62 sentiment: str = Field(
63 ..., description='Ticker sentiment analysis one of strong buy, buy, hold, sell or strong sell (case-sensitive)'
64 )
65 sentiment_reason: str = Field(
66 ...,
67 description=('Reason for the sentiment analysis. Short reasoning about the sentiment (1-2 sentences at most).'),
68 )
69 report: str = Field(..., description='Financial monitoring report')src/state.py
1"""Graph state module.
2
3This module contains the state definition for the agent graph.
4Resources:
5 - https://langchain-ai.github.io/langgraph/concepts/low_level/#state
6 - https://langchain-ai.github.io/langgraph/concepts/memory/
7"""
8
9from typing import Annotated, TypedDict
10
11from langgraph.graph.message import add_messages
12
13from src.models import OutputTickerReport
14
15
16class State(TypedDict):
17 """State of the agent graph."""
18
19 messages: Annotated[list, add_messages]
20
21 status: str
22 """Current status message of the Actor to be displayed."""
23 ticker: str
24 """Ticker that is being analyzed."""
25 analysis: str
26 """Analysis and data about the ticker news, prices, and recommendations."""
27 report: OutputTickerReport
28 """Final report for the ticker for the user."""src/tools.py
1import datetime
2import logging
3
4from apify import Actor
5from langchain_core.tools import tool
6
7from src.models import TickerInfo, TickerNewsEntry, TickerPriceTarget, TickerRecommendationEntry
8from src.utils import get_yahoo_dataset_data
9
10logger = logging.getLogger('apify')
11
12
13@tool
14async def tool_get_google_news(query: str, date_from: str, max_items: int = 25) -> list[TickerNewsEntry]:
15 """Tool to get recent news from Google News (can be used to get news about a ticker).
16
17 Args:
18 query (str): Query string.
19 date_from (str): Date from which to get news in format 'YYYY-MM-DD'.
20 max_items (int): Maximum number of news items to return.
21
22 Returns:
23 list[TickerNewsEntry]: Recent news.
24
25 Raises:
26 RuntimeError: If dataset does not contain required fields.
27 """
28 logger.debug('Running tool: tool_get_google_news')
29
30 # check date, raises ValueError if invalid
31 datetime.datetime.strptime(date_from, '%Y-%m-%d') # noqa: DTZ007
32
33 run_input = {
34 'query': query,
35 'dateFrom': date_from,
36 'maxItems': max_items,
37 'extractImages': False,
38 'language': 'US:en',
39 }
40 if not (run := await Actor.apify_client.actor('lhotanova/google-news-scraper').call(run_input=run_input)):
41 msg = 'Failed to start the Actor lhotanova/google-news-scraper!'
42 raise RuntimeError(msg)
43
44 dataset_id = run['defaultDatasetId']
45 dataset_items: list[dict] = (await Actor.apify_client.dataset(dataset_id).list_items()).items
46
47 google_news = []
48 for entry in dataset_items:
49 title = entry.get('title')
50 published_at = entry.get('publishedAt')
51 provider = entry.get('source')
52 url = entry.get('link')
53
54 if not all([title, published_at, provider, url]):
55 logger.warning('Skipping news entry with missing fields: %s', entry)
56 continue
57 google_news.append(
58 TickerNewsEntry(
59 title=str(title),
60 published_at=str(published_at),
61 provider=str(provider),
62 url=str(url),
63 )
64 )
65 return google_news
66
67
68@tool
69async def tool_get_yahoo_ticker_news(ticker: str) -> list[TickerNewsEntry]:
70 """Tool to get recent news from Yahoo Finance about a ticker.
71
72 Args:
73 ticker (str): Ticker symbol, for example 'TSLA'.
74
75 Returns:
76 list[TickerNewsEntry]: Recent news about the ticker.
77
78 Raises:
79 RuntimeError: If dataset does not contain required fields.
80 """
81 logger.debug('Running tool: tool_get_yahoo_ticker_news')
82 run_input = {
83 'process': 'gn',
84 'tickers': [f'{ticker}'],
85 }
86 if not (run := await Actor.apify_client.actor('canadesk/yahoo-finance').call(run_input=run_input)):
87 msg = 'Failed to start the Actor canadesk/yahoo-finance!'
88 raise RuntimeError(msg)
89
90 dataset_id = run['defaultDatasetId']
91 result = await get_yahoo_dataset_data(dataset_id)
92
93 ticker_news = []
94 for entry in result.get('data', []):
95 content = entry.get('content', {})
96 title = content.get('title')
97 summary = content.get('summary')
98 published_at = content.get('pubDate')
99 provider = content.get('provider', {}).get('displayName')
100 url = content.get('canonicalUrl', {}).get('url')
101
102 if not all([title, summary, published_at, provider, url]):
103 logger.warning('Skipping news entry with missing fields: %s', entry)
104 continue
105 ticker_news.append(
106 TickerNewsEntry(
107 title=title,
108 summary=summary,
109 published_at=published_at,
110 provider=provider,
111 url=url,
112 )
113 )
114 return ticker_news
115
116
117@tool
118async def tool_get_ticker_price_targets(ticker: str) -> TickerPriceTarget | str:
119 """Tool to get current price targets (analysis) for a ticker.
120
121 Args:
122 ticker (str): Ticker symbol, for example 'TSLA'.
123 output (Literal['str', 'model']): Output format.
124 'str' - return as string.
125 'model' - return as Pydantic model.
126
127 Returns:
128 TickerPriceTarget: Current price targets.
129
130 Raises:
131 RuntimeError: If dataset does not contain required fields.
132 """
133 logger.debug('Running tool: tool_get_ticker_price_targets')
134 run_input = {
135 'process': 'gp',
136 'tickers': [f'{ticker}'],
137 }
138 if not (run := await Actor.apify_client.actor('canadesk/yahoo-finance').call(run_input=run_input)):
139 msg = 'Failed to start the Actor canadesk/yahoo-finance!'
140 raise RuntimeError(msg)
141
142 dataset_id = run['defaultDatasetId']
143 result = await get_yahoo_dataset_data(dataset_id)
144
145 result.update(result.get('data', {}))
146 del result['data']
147
148 fields = ['ticker', 'current', 'low', 'high', 'mean', 'median']
149 if not all(f in result for f in fields):
150 msg = (
151 f'Dataset "{dataset_id}" does not contain required fields {fields}! '
152 f'It is possible that the ticker "{ticker}" is incorrect.'
153 )
154 raise RuntimeError(msg)
155
156 return TickerPriceTarget(
157 current_price=result['current'],
158 analyst_price_target_low=result['low'],
159 analyst_price_target_high=result['high'],
160 analyst_price_target_mean=result['mean'],
161 analyst_price_target_median=result['median'],
162 )
163
164
165@tool
166async def tool_get_ticker_basic_info(ticker: str) -> TickerInfo:
167 """Tool to get basic information about a ticker.
168
169 Args:
170 ticker (str): Ticker symbol, for example 'TSLA'.
171
172 Returns:
173 TickerInfo: Basic information about the ticker.
174
175 Raises:
176 RuntimeError: If dataset does not contain required fields.
177 """
178 logger.debug('Running tool: tool_get_ticker_basic_info')
179 run_input = {
180 'process': 'gi',
181 'tickers': [f'{ticker}'],
182 }
183 if not (run := await Actor.apify_client.actor('canadesk/yahoo-finance').call(run_input=run_input)):
184 msg = 'Failed to start the Actor canadesk/yahoo-finance!'
185 raise RuntimeError(msg)
186
187 dataset_id = run['defaultDatasetId']
188 result = await get_yahoo_dataset_data(dataset_id)
189
190 if not (data := result.get('data')):
191 msg = f'Failed to get data from dataset "{dataset_id}"! Dataset does not contain "data" field.'
192 raise RuntimeError(msg)
193 # flatten dataset
194 data['description'] = data.get('longBusinessSummary')
195 result.update(data)
196 del result['data']
197
198 fields = ['ticker', 'sector', 'industry', 'description']
199 if not all(f in result for f in fields):
200 msg = (
201 f'Dataset "{dataset_id}" does not contain required fields {fields}! '
202 f'It is possible that the ticker "{ticker}" is incorrect.'
203 )
204 raise RuntimeError(msg)
205
206 return TickerInfo(**{f: result[f] for f in fields})
207
208
209@tool
210async def tool_get_ticker_recommendations(ticker: str) -> list[TickerRecommendationEntry]:
211 """Tool to get recommendations for a ticker.
212
213 Args:
214 ticker (str): Ticker symbol, for example 'TSLA'.
215
216 Returns:
217 list[TickerRecommendationEntry]: Recommendations for the ticker.
218
219 Raises:
220 RuntimeError: If dataset does not contain required fields.
221 """
222 logger.debug('Running tool: tool_get_ticker_recommendations')
223 run_input = {
224 'process': 'gr',
225 'tickers': [f'{ticker}'],
226 }
227 if not (run := await Actor.apify_client.actor('canadesk/yahoo-finance').call(run_input=run_input)):
228 msg = 'Failed to start the Actor canadesk/yahoo-finance!'
229 raise RuntimeError(msg)
230
231 dataset_id = run['defaultDatasetId']
232 result = await get_yahoo_dataset_data(dataset_id)
233
234 ticker_recommendations = []
235 for entry in result.get('data', []):
236 period = entry.get('period')
237 strongbuy = entry.get('strongbuy')
238 buy = entry.get('buy')
239 hold = entry.get('hold')
240 sell = entry.get('sell')
241 strongsell = entry.get('strongsell')
242
243 if not all([period, strongbuy, buy, hold, sell, strongsell]):
244 logger.warning('Skipping recommendation entry with missing fields: %s', entry)
245 ticker_recommendations.append(
246 TickerRecommendationEntry(
247 period=period,
248 recommendations_strong_buy=strongbuy,
249 recommendations_buy=buy,
250 recommendations_hold=hold,
251 recommendations_sell=sell,
252 recommendations_strong_sell=strongsell,
253 )
254 )
255
256 return ticker_recommendationssrc/utils.py
1from apify import Actor
2
3
4async def get_yahoo_dataset_data(dataset_id: str) -> dict:
5 """Retrieve data from Yahoo Actor run Apify dataset.
6
7 Args:
8 dataset_id (str): Dataset ID.
9
10 Returns:
11 dict: Dataset record with the data.
12
13 Raises:
14 RuntimeError: If dataset retrieval fails.
15 ValueError: If dataset is empty.
16 """
17 dataset_items: list[dict] = (await Actor.apify_client.dataset(dataset_id).list_items()).items
18 if not dataset_items:
19 msg = f'Failed to get dataset "{dataset_id}"!'
20 raise RuntimeError(msg)
21
22 if len(dataset_items) == 0:
23 msg = f'Failed to get data from dataset "{dataset_id}", no items found!'
24 raise ValueError(msg)
25
26 return dataset_items[0]docs/report.md
1# Tesla, Inc. Financial Report - February 11, 2025
2
3## Executive Summary
4Tesla, Inc., under the leadership of Elon Musk, continues to be a significant player in the electric vehicle and clean energy sectors. Recent developments suggest challenges in EV adoption rates and potential strategic shifts with Musk's involvement in other ventures, which may impact the company's focus and resources. Despite this, Tesla remains positioned for growth, pending successful navigation of industry and competitive pressures.
5
6## Important Events
71. **Slowing EV Adoption in the U.S.:** External political factors have contributed to a reduced pace of electric vehicle adoption, which could potentially impact Tesla's future expansion.
82. **Elon Musk's Interest in OpenAI Acquisition:** Musk's potential acquisition of OpenAI has raised questions regarding the allocation of his focus and resources, indirectly affecting Tesla.
93. **Autonomous Driving Developments:** There is considerable anticipation about Tesla's launch of fully autonomous driving technology by mid-2025, a key area of future growth.
104. **Increased Competition:** Innovation by competitors such as BYD in China poses a challenge with their advancements in intelligent driving technology.
11
12## Stock Price
13- **Current Stock Price:** $350.73
14- **Price Targets:**
15 - **Low:** $135.00
16 - **High:** $550.00
17 - **Mean:** $336.42
18 - **Median:** $325.00
19
20## Analyst Recommendations
21- **Strong Buy:** 7
22- **Buy:** 12
23- **Hold:** 15
24- **Sell:** 9
25- **Strong Sell:** 3
26
27## Conclusion
28Tesla's stock is facing mixed sentiment due to external political influences, potential changes in strategic direction by Elon Musk, and heightened competition. Although challenges exist, Tesla's continued innovation in the EV and autonomous driving sector keeps it an interesting choice for stakeholders who are willing to manage risk in the evolving market.
29
30This report is generated by an AI agent and should not be considered as financial advice.tests/__init__.py
1import sys
2from pathlib import Path
3
4parent_dir = Path(__file__).parent
5src_dir = parent_dir.parent / 'src'
6
7sys.path.append(str(src_dir))tests/test_graph.py
1from langgraph.graph.state import CompiledStateGraph
2
3from src.graph import build_compiled_graph
4
5
6def test_build_compiled_graph() -> None:
7 """Test if the graph is built without errors."""
8 graph = build_compiled_graph()
9 assert isinstance(graph, CompiledStateGraph).github/workflows/run_code_checks.yml
1name: Code checks
2
3on:
4 pull_request:
5 types: [opened, synchronize, reopened]
6
7jobs:
8 code_check:
9 runs-on: ubuntu-latest
10 strategy:
11 matrix:
12 python-version: ["3.13"]
13 uv-version: ["latest"]
14 steps:
15 - name: Checkout repository
16 uses: actions/checkout@v2
17
18 - name: Set up Python
19 uses: actions/setup-python@v2
20 with:
21 python-version: ${{ matrix.python-version }}
22
23 - name: Install a specific version of uv
24 uses: astral-sh/setup-uv@v5
25 with:
26 version: ${{ matrix.uv-version }}
27
28 - name: Install dependencies
29 run: uv sync --frozen --group dev
30
31 - name: Run lint
32 run: make lint
33
34 - name: Run type check
35 run: make type-check
36
37 - name: Run tests
38 run: make unit-testDeveloper
Jakub KopeckýMaintained by Community
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Created in Feb 2025
Modified 6 hours ago
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