diff --git a/tradingagents/agents/analysts/fundamentals_analyst.py b/tradingagents/agents/analysts/fundamentals_analyst.py
index d1fdd3d8..2a21a457 100644
--- a/tradingagents/agents/analysts/fundamentals_analyst.py
+++ b/tradingagents/agents/analysts/fundamentals_analyst.py
@@ -1,15 +1,11 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
 from tradingagents.agents.utils.agent_utils import get_fundamentals, get_whitepaper, get_market_cap
-from tradingagents.dataflows.config import get_config
 
 
 def create_fundamentals_analyst(llm):
     def fundamentals_analyst_node(state):
         current_date = state["trade_date"]
         ticker = state["ticker_of_interest"]
-        company_name = state["ticker_of_interest"]
 
         tools = [
             get_fundamentals,
diff --git a/tradingagents/agents/analysts/market_analyst.py b/tradingagents/agents/analysts/market_analyst.py
index 8f54b988..95f80581 100644
--- a/tradingagents/agents/analysts/market_analyst.py
+++ b/tradingagents/agents/analysts/market_analyst.py
@@ -16,20 +16,20 @@ def create_market_analyst(llm):
         system_message = (
             """You are a crypto trading assistant tasked with analyzing cryptocurrency markets. Your role is to select the **most relevant indicators** for a given crypto market condition or trading strategy from the following list. The goal is to choose the **most effective indicators** that provide complementary insights without redundancy. Available indicators are:
 
-Moving Averages:
-- sma: Simple Moving Average: A basic trend-following indicator that smooths out price data. Usage: Identify trend direction and serve as dynamic support/resistance levels. Tips: Use multiple SMAs for crossover signals; combines well with volume analysis for confirmation.
+            Moving Averages:
+            - sma: Simple Moving Average: A basic trend-following indicator that smooths out price data. Usage: Identify trend direction and serve as dynamic support/resistance levels. Tips: Use multiple SMAs for crossover signals; combines well with volume analysis for confirmation.
 
-MACD Related:
-- macd: MACD (Moving Average Convergence Divergence): Measures momentum via differences between fast and slow EMAs. Usage: Look for signal line crossovers, centerline crossovers, and divergence patterns for trend changes. Tips: Most effective in trending markets; combine with RSI to avoid false signals in sideways markets.
+            MACD Related:
+            - macd: MACD (Moving Average Convergence Divergence): Measures momentum via differences between fast and slow EMAs. Usage: Look for signal line crossovers, centerline crossovers, and divergence patterns for trend changes. Tips: Most effective in trending markets; combine with RSI to avoid false signals in sideways markets.
 
-Momentum Indicators:
-- rsi: RSI (Relative Strength Index): Oscillator measuring momentum to identify overbought (>70) and oversold (<30) conditions. Usage: Look for reversal signals at extreme levels and divergence with price action. Tips: In strong crypto trends, RSI can remain extreme for extended periods; always confirm with trend analysis.
+            Momentum Indicators:
+            - rsi: RSI (Relative Strength Index): Oscillator measuring momentum to identify overbought (>70) and oversold (<30) conditions. Usage: Look for reversal signals at extreme levels and divergence with price action. Tips: In strong crypto trends, RSI can remain extreme for extended periods; always confirm with trend analysis.
 
-Volatility Indicators:
-- bbands: Bollinger Bands: Volatility indicator consisting of upper, middle (SMA), and lower bands based on standard deviations. Usage: Identify overbought/oversold conditions, volatility expansion/contraction, and potential breakout zones. Tips: Price touching bands doesn't guarantee reversal; use band squeeze for volatility breakout trades.
-- atr: ATR (Average True Range): Measures market volatility by calculating the average of true ranges over a period. Usage: Set stop-loss levels, position sizing, and identify high/low volatility periods for strategy adjustment. Tips: Higher ATR indicates more volatile conditions; use for risk management rather than directional signals.
+            Volatility Indicators:
+            - bbands: Bollinger Bands: Volatility indicator consisting of upper, middle (SMA), and lower bands based on standard deviations. Usage: Identify overbought/oversold conditions, volatility expansion/contraction, and potential breakout zones. Tips: Price touching bands doesn't guarantee reversal; use band squeeze for volatility breakout trades.
+            - atr: ATR (Average True Range): Measures market volatility by calculating the average of true ranges over a period. Usage: Set stop-loss levels, position sizing, and identify high/low volatility periods for strategy adjustment. Tips: Higher ATR indicates more volatile conditions; use for risk management rather than directional signals.
 
-- Select indicators that provide diverse and complementary information. Avoid redundancy and focus on the most effective combination for crypto market analysis. Also briefly explain why they are suitable for the given crypto 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_crypto_data first to retrieve the cryptocurrency price data. Then use get_indicators_bulk with a list of the specific indicator names (e.g., ["sma", "rsi", "macd"]). 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 crypto traders make decisions."""
+            - Select indicators that provide diverse and complementary information. Avoid redundancy and focus on the most effective combination for crypto market analysis. Also briefly explain why they are suitable for the given crypto 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_crypto_data first to retrieve the cryptocurrency price data. Then use get_indicators_bulk with a list of the specific indicator names (e.g., ["sma", "rsi", "macd"]). 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 crypto 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."""
         )
 
diff --git a/tradingagents/agents/analysts/news_analyst.py b/tradingagents/agents/analysts/news_analyst.py
index 64ddef4c..d8137acc 100644
--- a/tradingagents/agents/analysts/news_analyst.py
+++ b/tradingagents/agents/analysts/news_analyst.py
@@ -1,9 +1,5 @@
 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):
     def news_analyst_node(state):
diff --git a/tradingagents/agents/analysts/social_media_analyst.py b/tradingagents/agents/analysts/social_media_analyst.py
index cf2ccad0..269c6c10 100644
--- a/tradingagents/agents/analysts/social_media_analyst.py
+++ b/tradingagents/agents/analysts/social_media_analyst.py
@@ -1,15 +1,11 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
 from tradingagents.agents.utils.agent_utils import get_news, get_fear_and_greed
-from tradingagents.dataflows.config import get_config
 
 
 def create_social_media_analyst(llm):
     def social_media_analyst_node(state):
         current_date = state["trade_date"]
         ticker = state["ticker_of_interest"]
-        coin_name = state["ticker_of_interest"]
 
         tools = [
             get_news,
diff --git a/tradingagents/agents/managers/research_manager.py b/tradingagents/agents/managers/research_manager.py
index c537fa2f..d2cddd76 100644
--- a/tradingagents/agents/managers/research_manager.py
+++ b/tradingagents/agents/managers/research_manager.py
@@ -1,8 +1,6 @@
-import time
-import json
+from tradingagents.agents.utils.memory import FinancialSituationMemory
 
-
-def create_research_manager(llm, memory):
+def create_research_manager(llm, memory: FinancialSituationMemory):
     def research_manager_node(state) -> dict:
         history = state["investment_debate_state"].get("history", "")
         market_research_report = state["market_report"]
diff --git a/tradingagents/agents/managers/risk_manager.py b/tradingagents/agents/managers/risk_manager.py
index fba763d6..e251f3c5 100644
--- a/tradingagents/agents/managers/risk_manager.py
+++ b/tradingagents/agents/managers/risk_manager.py
@@ -1,12 +1,8 @@
-import time
-import json
+from tradingagents.agents.utils.memory import FinancialSituationMemory
 
-
-def create_risk_manager(llm, memory):
+def create_risk_manager(llm, memory: FinancialSituationMemory):
     def risk_manager_node(state) -> dict:
 
-        company_name = state["company_of_interest"]
-
         history = state["risk_debate_state"]["history"]
         risk_debate_state = state["risk_debate_state"]
         market_research_report = state["market_report"]
diff --git a/tradingagents/agents/researchers/bear_researcher.py b/tradingagents/agents/researchers/bear_researcher.py
index cfaf06e9..6ee5d8e3 100644
--- a/tradingagents/agents/researchers/bear_researcher.py
+++ b/tradingagents/agents/researchers/bear_researcher.py
@@ -1,9 +1,6 @@
-from langchain_core.messages import AIMessage
-import time
-import json
+from tradingagents.agents.utils.memory import FinancialSituationMemory
 
-
-def create_bear_researcher(llm, memory):
+def create_bear_researcher(llm, memory: FinancialSituationMemory):
     def bear_node(state) -> dict:
         investment_debate_state = state["investment_debate_state"]
         history = investment_debate_state.get("history", "")
@@ -37,7 +34,7 @@ Resources available:
 Market research report: {market_research_report}
 Social media sentiment report: {sentiment_report}
 Latest world affairs news: {news_report}
-Company fundamentals report: {fundamentals_report}
+Coin fundamentals report: {fundamentals_report}
 Conversation history of the debate: {history}
 Last bull argument: {current_response}
 Reflections from similar situations and lessons learned: {past_memory_str}
diff --git a/tradingagents/agents/researchers/bull_researcher.py b/tradingagents/agents/researchers/bull_researcher.py
index 6bc07dff..e5be2337 100644
--- a/tradingagents/agents/researchers/bull_researcher.py
+++ b/tradingagents/agents/researchers/bull_researcher.py
@@ -1,9 +1,6 @@
-from langchain_core.messages import AIMessage
-import time
-import json
+from tradingagents.agents.utils.memory import FinancialSituationMemory
 
-
-def create_bull_researcher(llm, memory):
+def create_bull_researcher(llm, memory: FinancialSituationMemory):
     def bull_node(state) -> dict:
         investment_debate_state = state["investment_debate_state"]
         history = investment_debate_state.get("history", "")
diff --git a/tradingagents/agents/risk_mgmt/aggresive_debator.py b/tradingagents/agents/risk_mgmt/aggresive_debator.py
index 8363b0ad..575da9f5 100644
--- a/tradingagents/agents/risk_mgmt/aggresive_debator.py
+++ b/tradingagents/agents/risk_mgmt/aggresive_debator.py
@@ -1,7 +1,3 @@
-import time
-import json
-
-
 def create_risky_debator(llm):
     def risky_node(state) -> dict:
         risk_debate_state = state["risk_debate_state"]
diff --git a/tradingagents/agents/risk_mgmt/conservative_debator.py b/tradingagents/agents/risk_mgmt/conservative_debator.py
index 961a786e..c863351b 100644
--- a/tradingagents/agents/risk_mgmt/conservative_debator.py
+++ b/tradingagents/agents/risk_mgmt/conservative_debator.py
@@ -1,8 +1,3 @@
-from langchain_core.messages import AIMessage
-import time
-import json
-
-
 def create_safe_debator(llm):
     def safe_node(state) -> dict:
         risk_debate_state = state["risk_debate_state"]
diff --git a/tradingagents/agents/risk_mgmt/neutral_debator.py b/tradingagents/agents/risk_mgmt/neutral_debator.py
index 6a5ff5c5..efc48c3f 100644
--- a/tradingagents/agents/risk_mgmt/neutral_debator.py
+++ b/tradingagents/agents/risk_mgmt/neutral_debator.py
@@ -1,7 +1,3 @@
-import time
-import json
-
-
 def create_neutral_debator(llm):
     def neutral_node(state) -> dict:
         risk_debate_state = state["risk_debate_state"]
diff --git a/tradingagents/agents/trader/trader.py b/tradingagents/agents/trader/trader.py
index e58e5eae..167c0b6a 100644
--- a/tradingagents/agents/trader/trader.py
+++ b/tradingagents/agents/trader/trader.py
@@ -1,7 +1,4 @@
 import functools
-import time
-import json
-
 
 def create_trader(llm, memory):
     def trader_node(state, name):
diff --git a/tradingagents/agents/utils/agent_states.py b/tradingagents/agents/utils/agent_states.py
index 4f6c7f34..b1541db3 100644
--- a/tradingagents/agents/utils/agent_states.py
+++ b/tradingagents/agents/utils/agent_states.py
@@ -1,10 +1,7 @@
-from typing import Annotated, Sequence
-from datetime import date, timedelta, datetime
-from typing_extensions import TypedDict, Optional
-from langchain_openai import ChatOpenAI
+from typing import Annotated
+from typing_extensions import TypedDict
 from tradingagents.agents import *
-from langgraph.prebuilt import ToolNode
-from langgraph.graph import END, StateGraph, START, MessagesState
+from langgraph.graph import MessagesState
 
 
 # Researcher team state
@@ -48,7 +45,6 @@ class RiskDebateState(TypedDict):
 
 
 class AgentState(MessagesState):
-    company_of_interest: Annotated[str, "Company that we are interested in trading"]
     ticker_of_interest: Annotated[str, "Ticker that we are interested in trading"] # e.g BTC/USDT
     trade_date: Annotated[str, "What date we are trading at"]
 
diff --git a/tradingagents/graph/propagation.py b/tradingagents/graph/propagation.py
index bee1d8fb..c5d119dc 100644
--- a/tradingagents/graph/propagation.py
+++ b/tradingagents/graph/propagation.py
@@ -21,7 +21,6 @@ class Propagator:
         """Create the initial state for the agent graph."""
         return {
             "messages": [("human", ticker)],
-            "company_of_interest": ticker,
             "ticker_of_interest": ticker,
             "trade_date": str(trade_date),
             "investment_debate_state": InvestDebateState(
diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py
index 27b5e8f5..1307423e 100644
--- a/tradingagents/graph/trading_graph.py
+++ b/tradingagents/graph/trading_graph.py
@@ -206,7 +206,7 @@ class TradingAgentsGraph:
     def _log_state(self, trade_date, final_state):
         """Log the final state to a JSON file."""
         self.log_states_dict[str(trade_date)] = {
-            "company_of_interest": final_state["company_of_interest"],
+            "ticker_of_interest": final_state["ticker_of_interest"],
             "trade_date": final_state["trade_date"],
             "market_report": final_state["market_report"],
             "sentiment_report": final_state["sentiment_report"],