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feat: add stop-loss and take-profit functionality #10

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feat: add stop-loss and take-profit functionality #10

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8 changes: 8 additions & 0 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -76,12 +76,20 @@ You can also specify a `--show-reasoning` flag to print the reasoning of each ag
```bash
poetry run python src/agents.py --ticker AAPL --show-reasoning
```

You can optionally specify the start and end dates to make decisions for a specific time period.

```bash
poetry run python src/agents.py --ticker AAPL --start-date 2024-01-01 --end-date 2024-03-01
```

Risk management parameters can be configured using:
```bash
poetry run python src/agents.py --ticker AAPL --stop-loss 0.05 --take-profit 0.15
```
- `--stop-loss`: Stop loss percentage (default: 5%)
- `--take-profit`: Take profit percentage (default: 15%)

### Running the Backtester

```bash
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102 changes: 87 additions & 15 deletions src/agents.py
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Original file line number Diff line number Diff line change
Expand Up @@ -402,6 +402,7 @@ def risk_management_agent(state: AgentState):
data = state["data"]

prices_df = prices_to_df(data["prices"])
current_price = prices_df['close'].iloc[-1]

# Fetch messages from other agents
quant_message = next(msg for msg in state["messages"] if msg.name == "quant_agent")
Expand All @@ -417,7 +418,6 @@ def risk_management_agent(state: AgentState):
technical_signals = ast.literal_eval(quant_message.content)
sentiment_signals = ast.literal_eval(sentiment_message.content)


print(f"fundamental_signals: {fundamental_signals}")
print(f"technical_signals: {technical_signals}")
print(f"sentiment_signals: {sentiment_signals}")
Expand Down Expand Up @@ -459,7 +459,7 @@ def risk_management_agent(state: AgentState):

# 3. Position Size Limits
# Consider total portfolio value, not just cash
current_stock_value = portfolio['stock'] * prices_df['close'].iloc[-1]
current_stock_value = portfolio['stock'] * current_price
total_portfolio_value = portfolio['cash'] + current_stock_value

base_position_size = total_portfolio_value * 0.25 # Start with 25% max position of total portfolio
Expand All @@ -474,7 +474,30 @@ def risk_management_agent(state: AgentState):
# Keep base size for low risk
max_position_size = base_position_size

# 4. Stress Testing
# 4. Stop-Loss and Take-Profit Check
stop_loss_triggered = False
take_profit_triggered = False
price_action_signal = "hold"
stop_loss_price = None
take_profit_price = None

if portfolio['stock'] > 0 and portfolio['entry_price'] is not None:
# Calculate current return
current_return = (current_price - portfolio['entry_price']) / portfolio['entry_price']

# Check stop-loss
if current_return <= -portfolio['stop_loss']:
stop_loss_triggered = True
price_action_signal = "sell"
stop_loss_price = portfolio['entry_price'] * (1 - portfolio['stop_loss'])

# Check take-profit
elif current_return >= portfolio['take_profit']:
take_profit_triggered = True
price_action_signal = "sell"
take_profit_price = portfolio['entry_price'] * (1 + portfolio['take_profit'])

# 5. Stress Testing
stress_test_scenarios = {
"market_crash": -0.20,
"moderate_decline": -0.10,
Expand All @@ -492,7 +515,7 @@ def risk_management_agent(state: AgentState):
"portfolio_impact": portfolio_impact
}

# 5. Risk-Adjusted Signals Analysis
# 6. Risk-Adjusted Signals Analysis
# Convert all confidences to numeric for proper comparison
def parse_confidence(conf_str):
return float(conf_str.replace('%', '')) / 100.0
Expand All @@ -511,10 +534,12 @@ def parse_confidence(conf_str):
# Cap risk score at 10
risk_score = min(round(risk_score), 10)

# 6. Generate Trading Action
# 7. Generate Trading Action
# If risk is very high, hold. If moderately high, consider reducing.
# Else, follow fundamental signal as a baseline.
if risk_score >= 8:
if stop_loss_triggered or take_profit_triggered:
trading_action = price_action_signal
elif risk_score >= 8:
trading_action = "hold"
elif risk_score >= 6:
trading_action = "reduce"
Expand All @@ -530,11 +555,20 @@ def parse_confidence(conf_str):
"value_at_risk_95": float(var_95),
"max_drawdown": float(max_drawdown),
"market_risk_score": market_risk_score,
"stress_test_results": stress_test_results
"stress_test_results": stress_test_results,
"stop_loss_triggered": stop_loss_triggered,
"take_profit_triggered": take_profit_triggered,
"stop_loss_price": stop_loss_price,
"take_profit_price": take_profit_price,
"current_price": current_price
},
"reasoning": f"Risk Score {risk_score}/10: Market Risk={market_risk_score}, "
f"Volatility={volatility:.2%}, VaR={var_95:.2%}, "
f"Max Drawdown={max_drawdown:.2%}"
"reasoning": (
f"Risk Score {risk_score}/10: Market Risk={market_risk_score}, "
f"Volatility={volatility:.2%}, VaR={var_95:.2%}, "
f"Max Drawdown={max_drawdown:.2%}"
+ (f", Stop-Loss Triggered at {stop_loss_price:.2f}" if stop_loss_triggered else "")
+ (f", Take-Profit Triggered at {take_profit_price:.2f}" if take_profit_triggered else "")
)
}

# Create the risk management message
Expand All @@ -554,6 +588,11 @@ def portfolio_management_agent(state: AgentState):
"""Makes final trading decisions and generates orders"""
show_reasoning = state["metadata"]["show_reasoning"]
portfolio = state["data"]["portfolio"]
data = state["data"]

# Get current price
prices_df = prices_to_df(data["prices"])
current_price = prices_df['close'].iloc[-1]

# Get the quant agent, fundamentals agent, and risk management agent messages
quant_message = next(msg for msg in state["messages"] if msg.name == "quant_agent")
Expand All @@ -574,6 +613,7 @@ def portfolio_management_agent(state: AgentState):
- You MUST NOT exceed the max_position_size specified by the risk manager
- You MUST follow the trading_action (buy/sell/hold) recommended by risk management
- These are hard constraints that cannot be overridden by other signals
- If stop-loss or take-profit is triggered, you MUST execute the sell order

When weighing the different signals for direction and timing:
1. Fundamental Analysis (50% weight)
Expand All @@ -589,7 +629,7 @@ def portfolio_management_agent(state: AgentState):
- Can influence sizing within risk limits

The decision process should be:
1. First check risk management constraints
1. First check risk management constraints and stop-loss/take-profit
2. Then evaluate fundamental outlook
3. Use technical analysis for timing
4. Consider sentiment for final adjustment
Expand All @@ -606,7 +646,8 @@ def portfolio_management_agent(state: AgentState):
- Only buy if you have available cash
- Only sell if you have shares to sell
- Quantity must be ≤ current position for sells
- Quantity must be ≤ max_position_size from risk management"""
- Quantity must be ≤ max_position_size from risk management
- If stop-loss or take-profit triggered, sell entire position"""
),
(
"human",
Expand All @@ -621,6 +662,8 @@ def portfolio_management_agent(state: AgentState):
Portfolio:
Cash: {portfolio_cash}
Current Position: {portfolio_stock} shares
Current Price: {current_price}
Entry Price: {entry_price}

Only include the action, quantity, reasoning, confidence, and agent_signals in your output as JSON. Do not include any JSON markdown.

Expand All @@ -640,15 +683,31 @@ def portfolio_management_agent(state: AgentState):
"sentiment_message": sentiment_message.content,
"risk_message": risk_message.content,
"portfolio_cash": f"{portfolio['cash']:.2f}",
"portfolio_stock": portfolio["stock"]
"portfolio_stock": portfolio["stock"],
"current_price": f"{current_price:.2f}",
"entry_price": f"{portfolio['entry_price']:.2f}" if portfolio['entry_price'] is not None else "None"
}
)

# Invoke the LLM
result = llm.invoke(prompt)

# Parse the decision
try:
decision = json.loads(result.content)
# Update entry price if buying into a new position
if decision["action"] == "buy" and portfolio["stock"] == 0:
portfolio["entry_price"] = current_price
# Reset entry price if selling entire position
elif decision["action"] == "sell" and decision["quantity"] >= portfolio["stock"]:
portfolio["entry_price"] = None
except Exception as e:
print(f"Error parsing decision: {e}")
decision = {"action": "hold", "quantity": 0, "confidence": 0.0, "reasoning": "Error parsing decision"}

# Create the portfolio management message
message = HumanMessage(
content=result.content,
content=json.dumps(decision),
name="portfolio_management",
)

Expand Down Expand Up @@ -726,6 +785,8 @@ def run_hedge_fund(ticker: str, start_date: str, end_date: str, portfolio: dict,
parser.add_argument('--start-date', type=str, help='Start date (YYYY-MM-DD). Defaults to 3 months before end date')
parser.add_argument('--end-date', type=str, help='End date (YYYY-MM-DD). Defaults to today')
parser.add_argument('--show-reasoning', action='store_true', help='Show reasoning from each agent')
parser.add_argument('--stop-loss', type=float, default=0.05, help='Stop loss percentage (default: 5%%)')
parser.add_argument('--take-profit', type=float, default=0.15, help='Take profit percentage (default: 15%%)')

args = parser.parse_args()

Expand All @@ -742,10 +803,21 @@ def run_hedge_fund(ticker: str, start_date: str, end_date: str, portfolio: dict,
except ValueError:
raise ValueError("End date must be in YYYY-MM-DD format")

# Validate stop-loss and take-profit
if not (0 < args.stop_loss < 1):
raise ValueError("Stop loss must be between 0 and 1 (e.g., 0.05 for 5%)")
if not (0 < args.take_profit < 1):
raise ValueError("Take profit must be between 0 and 1 (e.g., 0.15 for 15%)")
if args.take_profit <= args.stop_loss:
raise ValueError("Take profit must be greater than stop loss")

# Sample portfolio - you might want to make this configurable too
portfolio = {
"cash": 100000.0, # $100,000 initial cash
"stock": 0 # No initial stock position
"stock": 0, # No initial stock position
"stop_loss": args.stop_loss,
"take_profit": args.take_profit,
"entry_price": None # Will be set when a position is opened
}

result = run_hedge_fund(
Expand Down