WIP

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -1,28 +1,27 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import time
 import json
+from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_insider_sentiment, get_insider_transactions
+from tradingagents.dataflows.config import get_config
 
 
-def create_fundamentals_analyst(llm, toolkit):
+def create_fundamentals_analyst(llm):
     def fundamentals_analyst_node(state):
         current_date = state["trade_date"]
         ticker = state["company_of_interest"]
         company_name = state["company_of_interest"]
 
-        if toolkit.config["online_tools"]:
-            tools = [toolkit.get_fundamentals_openai]
-        else:
-            tools = [
-                toolkit.get_finnhub_company_insider_sentiment,
-                toolkit.get_finnhub_company_insider_transactions,
-                toolkit.get_simfin_balance_sheet,
-                toolkit.get_simfin_cashflow,
-                toolkit.get_simfin_income_stmt,
-            ]
+        tools = [
+            get_fundamentals,
+            get_balance_sheet,
+            get_cashflow,
+            get_income_statement,
+        ]
 
         system_message = (
             "You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, company financial history, insider sentiment and insider transactions to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
-            + " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.",
+            + " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."
+            + " Use the get_fundamental_data tool with different data_type parameters: 'full_fundamentals' for comprehensive analysis, 'balance_sheet', 'cashflow', 'income_statement' for specific financial statements, 'insider_sentiment' and 'insider_transactions' for insider information.",
         )
 
         prompt = ChatPromptTemplate.from_messages(

tradingagents/agents/analysts/market_analyst.py

@@ -1,25 +1,21 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import time
 import json
+from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators
+from tradingagents.dataflows.config import get_config
 
 
-def create_market_analyst(llm, toolkit):
+def create_market_analyst(llm):
 
     def market_analyst_node(state):
         current_date = state["trade_date"]
         ticker = state["company_of_interest"]
         company_name = state["company_of_interest"]
 
-        if toolkit.config["online_tools"]:
-            tools = [
-                toolkit.get_YFin_data_online,
-                toolkit.get_stockstats_indicators_report_online,
-            ]
-        else:
-            tools = [
-                toolkit.get_YFin_data,
-                toolkit.get_stockstats_indicators_report,
-            ]
+        tools = [
+            get_stock_data,
+            get_indicators,
+        ]
 
         system_message = (
             """You are a trading assistant tasked with analyzing financial markets. Your role is to select the **most relevant indicators** for a given market condition or trading strategy from the following list. The goal is to choose up to **8 indicators** that provide complementary insights without redundancy. Categories and each category's indicators are:
@@ -46,7 +42,7 @@ Volatility Indicators:
 Volume-Based Indicators:
 - vwma: VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses.
 
-- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_YFin_data first to retrieve the CSV that is needed to generate indicators. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."""
+- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_technical_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."""
             + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
         )
 

tradingagents/agents/analysts/news_analyst.py

@@ -1,25 +1,23 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import time
 import json
+from tradingagents.agents.utils.agent_utils import get_news, get_global_news
+from tradingagents.dataflows.config import get_config
 
 
-def create_news_analyst(llm, toolkit):
+def create_news_analyst(llm):
     def news_analyst_node(state):
         current_date = state["trade_date"]
         ticker = state["company_of_interest"]
 
-        if toolkit.config["online_tools"]:
-            tools = [toolkit.get_global_news_openai, toolkit.get_google_news]
-        else:
-            tools = [
-                toolkit.get_finnhub_news,
-                toolkit.get_reddit_news,
-                toolkit.get_google_news,
-            ]
+        tools = [
+            get_news,
+            get_global_news,
+        ]
 
         system_message = (
-            "You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Look at news from EODHD, and finnhub to be comprehensive. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
-            + """ Make sure to append a Makrdown table at the end of the report to organize key points in the report, organized and easy to read."""
+            "You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the get_news_data tool with different news_type parameters: 'global_news' for macroeconomic news, 'company_news' for company-specific news, 'reddit_global' for social sentiment, 'google' for general news searches. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+            + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
         )
 
         prompt = ChatPromptTemplate.from_messages(

tradingagents/agents/analysts/social_media_analyst.py

@@ -1,24 +1,23 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 import time
 import json
+from tradingagents.agents.utils.agent_utils import get_news
+from tradingagents.dataflows.config import get_config
 
 
-def create_social_media_analyst(llm, toolkit):
+def create_social_media_analyst(llm):
     def social_media_analyst_node(state):
         current_date = state["trade_date"]
         ticker = state["company_of_interest"]
         company_name = state["company_of_interest"]
 
-        if toolkit.config["online_tools"]:
-            tools = [toolkit.get_stock_news_openai]
-        else:
-            tools = [
-                toolkit.get_reddit_stock_info,
-            ]
+        tools = [
+            get_news,
+        ]
 
         system_message = (
-            "You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
-            + """ Make sure to append a Makrdown table at the end of the report to organize key points in the report, organized and easy to read.""",
+            "You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Use get_news_data with news_type parameters: 'company_news' for company-specific news, 'reddit_stock' for Reddit discussions about the stock. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+            + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""",
         )
 
         prompt = ChatPromptTemplate.from_messages(