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Update total portfolio value error by breaking the loops #200

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136 changes: 80 additions & 56 deletions src/agents/risk_manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,77 +7,101 @@

##### Risk Management Agent #####
def risk_management_agent(state: AgentState):
"""Controls position sizing based on real-world risk factors for multiple tickers."""
portfolio = state["data"]["portfolio"]
data = state["data"]
tickers = data["tickers"]

# Initialize risk analysis for each ticker
risk_analysis = {}
current_prices = {} # Store prices here to avoid redundant API calls
"""Controls position sizing based on real-world risk factors for multiple tickers."""
portfolio = state["data"]["portfolio"]
data = state["data"]
tickers = data["tickers"]

# Initialize risk analysis for each ticker
risk_analysis = {}
current_prices = {} # Store prices here to avoid redundant API calls
cost_value = {}

# intitialize total portfolio to cash
total_portfolio_value = portfolio.get("cash", 0)

# get all pricing and calculate total portfolio value
for ticker in tickers:
progress.update_status("risk_management_agent", ticker, "Portfolio calculation with latest price data")

prices = get_prices(
ticker=ticker,
start_date=data["start_date"],
end_date=data["end_date"],
)

for ticker in tickers:
progress.update_status("risk_management_agent", ticker, "Analyzing price data")
if not prices:
progress.update_status("risk_management_agent", ticker, "Failed: No price data found")
continue

prices = get_prices(
ticker=ticker,
start_date=data["start_date"],
end_date=data["end_date"],
)
prices_df = prices_to_df(prices)
progress.update_status("risk_management_agent", ticker, "Calculating position limits")

if not prices:
progress.update_status("risk_management_agent", ticker, "Failed: No price data found")
continue
# Calculate portfolio value
current_price = prices_df["close"].iloc[-1]
current_prices[ticker] = current_price # Store the current price

prices_df = prices_to_df(prices)
# Calculate current position value for this ticker
current_position_value = portfolio["positions"][ticker]["long"] * current_prices[ticker]

progress.update_status("risk_management_agent", ticker, "Calculating position limits")
cost_value[ticker] = portfolio["positions"][ticker]["long"] * portfolio["positions"][ticker]["long_cost_basis"]

# Calculate portfolio value
current_price = prices_df["close"].iloc[-1]
current_prices[ticker] = current_price # Store the current price
print("Current position value of ", ticker, " is: ", current_position_value)
print("Cost basis value of ", ticker, " is: ", cost_value[ticker])

# Calculate current position value for this ticker
current_position_value = portfolio.get("cost_basis", {}).get(ticker, 0)
# Calculate total portfolio value using stored prices
total_portfolio_value = total_portfolio_value + current_position_value

# Calculate total portfolio value using stored prices
total_portfolio_value = portfolio.get("cash", 0) + sum(portfolio.get("cost_basis", {}).get(t, 0) for t in portfolio.get("cost_basis", {}))
print("Total portfolio value is: ", total_portfolio_value)

# Base limit is 20% of portfolio for any single position
position_limit = total_portfolio_value * 0.20
for ticker in tickers:
progress.update_status("risk_management_agent", ticker, "Managing position limits")

# For existing positions, subtract current position value from limit
remaining_position_limit = position_limit - current_position_value
current_position_value = portfolio["positions"][ticker]["long"] * current_prices[ticker]

# Ensure we don't exceed available cash
max_position_size = min(remaining_position_limit, portfolio.get("cash", 0))
# Base limit is 20% of portfolio for any single position
position_limit = total_portfolio_value * 0.20
print("Position limit for ", ticker, " is: ", position_limit)

risk_analysis[ticker] = {
"remaining_position_limit": float(max_position_size),
"current_price": float(current_price),
"reasoning": {
"portfolio_value": float(total_portfolio_value),
"current_position": float(current_position_value),
"position_limit": float(position_limit),
"remaining_limit": float(remaining_position_limit),
"available_cash": float(portfolio.get("cash", 0)),
},
}
# For existing positions, subtract current position value from limit
remaining_position_limit = position_limit - current_position_value
print("Remaining position limit for ", ticker, " is: ", remaining_position_limit)

progress.update_status("risk_management_agent", ticker, "Done")

message = HumanMessage(
content=json.dumps(risk_analysis),
name="risk_management_agent",
)
# Ensure we don't exceed available cash
if remaining_position_limit < 0:
remaining_position_limit = 0

if state["metadata"]["show_reasoning"]:
show_agent_reasoning(risk_analysis, "Risk Management Agent")
max_position_size = min(remaining_position_limit, portfolio.get("cash", 0))

# Add the signal to the analyst_signals list
state["data"]["analyst_signals"]["risk_management_agent"] = risk_analysis
print("Max position size for ", ticker, " is: ", max_position_size)

return {
"messages": state["messages"] + [message],
"data": data,
risk_analysis[ticker] = {
"remaining_position_limit": float(max_position_size),
"current_price": float(current_price),
"reasoning": {
"portfolio_value": float(total_portfolio_value),
"current_position": float(current_position_value),
"position_limit": float(position_limit),
"remaining_limit": float(remaining_position_limit),
"available_cash": float(portfolio.get("cash", 0)),
},
}

progress.update_status("risk_management_agent", ticker, "Done")

message = HumanMessage(
content=json.dumps(risk_analysis),
name="risk_management_agent",
)

if state["metadata"]["show_reasoning"]:
show_agent_reasoning(risk_analysis, "Risk Management Agent")

# Add the signal to the analyst_signals list
state["data"]["analyst_signals"]["risk_management_agent"] = risk_analysis

return {
"messages": state["messages"] + [message],
"data": data,
}